bluuebunny/crossref_metadata_2025 huggingface dataset:
Pay Attention: a Call to Regulate the Attention Market and Prevent Algorithmic Emotional Governance
Authors: Michel Franck , Gandon Fabien
Published: November 2024

Over the last 70 years, we, humans, have created an economic market where attention is being captured and turned into money thanks to advertising. During the last two decades, leveraging research in psychology, sociology, neuroscience and other domains, Web platforms have brought the process of capturing attention to an unprecedented scale. With the initial commonplace goal of making targeted advertising more effective, the generalization of attention-capturing techniques and their use of cognitive biases and emotions have multiple detrimental side effects such as polarizing opinions, spreading false information and threatening public health, economies and democracies. This is clearly a case where the Web is not used for the common good and where, in fact, all its users become a vulnerable population. This paper brings together contributions from a wide range of disciplines to analyze current practices and consequences thereof. Through a set of propositions and principles that could be used do drive further works, it calls for actions against these practices competing to capture our attention on the Web, as it would be unsustainable for a civilization to allow attention to be wasted with impunity on a world-wide scale.
Quantifying Gendered Citation Imbalance in Computer Science Conferences
Authors: Nakajima Kazuki , Sasaki Yuya , Tokuno Sohei , Fletcher George
Published: November 2024

The number of citations received by papers often exhibits imbalances in terms of author attributes such as country of affiliation and gender. While recent studies have quantified citation imbalance in terms of the authors' gender in journal papers, the computer science discipline, where researchers frequently present their work at conferences, may exhibit unique patterns in gendered citation imbalance. Additionally, understanding how network properties in citations influence citation imbalances remains challenging due to a lack of suitable reference models. In this paper, we develop a family of reference models for citation networks and investigate gender imbalance in citations between papers published in computer science conferences. By deploying these reference models, we found that homophily in citations is strongly associated with gendered citation imbalance in computer science, whereas heterogeneity in the number of citations received per paper has a relatively minor association with it. Furthermore, we found that the gendered citation imbalance is most pronounced in papers published in the highest-ranked conferences, is present across different subfields, and extends to citation-based rankings of papers. Our study provides a framework for investigating associations between network properties and citation imbalances, aiming to enhance our understanding of the structure and dynamics of citations between research publications.
Habemus a Right to an Explanation: so What? – A Framework on Transparency-Explainability Functionality and Tensions in the EU AI Act
Authors: Nannini Luca
Published: November 2024

The European Union's Artificial Intelligence Act (AI Act), finalized in February 2024, mandates comprehensive transparency and explainability requirements for AI systems to enable effective oversight and safeguard fundamental rights. However, the practical implementation of these requirements faces challenges due to tensions between the need for meaningful explanations and the potential risks to intellectual property and commercial interests of AI providers. This research proposes the Transparency-Explainability Functionality and Tensions (TEFT) framework to systematically analyze the complex interplay of legal, technical, and socio-ethical factors shaping the realization of algorithmic transparency and explainability in the EU context.
Through a two-pronged approach combining a focused literature review and an in-depth examination of the AI Act's provisions, we identify key friction points and challenges in operationalizing the right to explanation. The TEFT framework maps the interests and incentives of various stakeholders, including AI providers & deployers, oversight bodies, and affected individuals, while considering their goals, expected benefits, risks, possible negative impacts, and context to algorithmic explainability.
Human-Centered AI Applications for Canada’s Immigration Settlement Sector
Authors: Nejadgholi Isar , Molamohammadi Maryam , Missaghi Kimiya , Bakhtawar Samir
Published: November 2024

While AI has been frequently applied in the context of immigration, most of these applications focus on selection and screening, which primarily serve to empower states and authorities, raising concerns due to their understudied reliability and high impact on immigrants' lives. In contrast, this paper emphasizes the potential of AI in Canada’s immigration settlement phase, a stage where access to information is crucial and service providers are overburdened. By highlighting the settlement sector as a prime candidate for reliable AI applications, we demonstrate its unique capacity to empower immigrants directly, yet it remains under-explored in AI research. We outline a vision for human-centred and responsible AI solutions that facilitate the integration of newcomers. We call on AI researchers to build upon our work and engage in multidisciplinary research and active collaboration with service providers and government organizations to develop tailored AI tools that are empowering, inclusive and safe.
AIDE: Antithetical, Intent-based, and Diverse Example-Based Explanations
Authors: Nematov Ikhtiyor , Sacharidis Dimitris , Hose Katja , Sagi Tomer
Published: November 2024

For many use-cases, it is often important to explain the prediction of a black-box model by identifying the most influential training data samples.
Existing approaches lack customization for user intent and often provide a homogeneous set of explanation samples, failing to reveal the model's reasoning from different angles.

In this paper, we propose AIDE, an approach for providing antithetical (i.e., contrastive), intent-based, diverse explanations for opaque and complex models. AIDE distinguishes three types of explainability intents: interpreting a correct, investigating a wrong, and clarifying an ambiguous prediction. For each intent, AIDE selects an appropriate set of influential training samples that support or oppose the prediction either directly or by contrast.
To provide a succinct summary, AIDE uses diversity-aware sampling to avoid redundancy and increase coverage of the training data.

We demonstrate the effectiveness of AIDE on image and text classification tasks,
in three ways:
quantitatively, assessing correctness and continuity;
qualitatively, comparing anecdotal evidence from AIDE and other example-based approaches;
and via a user study, evaluating multiple aspects of AIDE.
The results show that AIDE addresses the limitations of existing methods and exhibits desirable traits for an explainability method.
Hidden or Inferred: Fair Learning-To-Rank With Unknown Demographics
Authors: Olulana Oluseun , Cachel Kathleen , Murai Fabricio , Rundensteiner Elke
Published: November 2024

As learning-to-rank models are increasingly deployed for decision-making in areas with profound life implications, the FairML community has been developing fair learning-to-rank (LTR) models. These models rely on the availability of sensitive demographic features such as race or sex. However, in practice, regulatory obstacles and privacy concerns protect this data from collection and use. As a result, practitioners may either need to promote fairness despite the absence of these features or turn to demographic inference tools to attempt to infer them. Given that these tools are fallible, this paper aims to further understand how errors in demographic inference impact the fairness performance of popular fair LTR strategies. In which cases would it be better to keep such demographic attributes hidden from models versus infer them? We examine a spectrum of fair LTR strategies ranging from fair LTR with and without demographic features hidden versus inferred to fairness-unaware LTR followed by fair re-ranking. We conduct a controlled empirical investigation modeling different levels of inference errors by systematically perturbing the inferred sensitive attribute. We also perform three case studies with real-world datasets and popular open-source inference methods. Our findings reveal that as inference noise grows, LTR-based methods that incorporate fairness considerations into the learning process may increase bias. In contrast, fair re-ranking strategies are more robust to inference errors. All source code, data, and experimental artifacts of our experimental study are available here: https://github.com/sewen007/hoiltr.git
Gaps in the Safety Evaluation of Generative AI
Authors: Rauh Maribeth , Marchal Nahema , Manzini Arianna , Hendricks Lisa Anne , Comanescu Ramona , Akbulut Canfer , Stepleton Tom , Mateos-Garcia Juan , Bergman Stevie , Kay Jackie , Griffin Conor , Bariach Ben , Gabriel Iason , Rieser Verena , Isaac William , Weidinger Laura
Published: November 2024

Generative AI systems produce a range of ethical and social risks. Evaluation of these risks is a critical step on the path to ensuring the safety of these systems. However, evaluation requires the availability of validated and established measurement approaches and tools. In this paper, we provide an empirical review of the methods and tools that are available for evaluating known safety of generative AI systems to date. To this end, we review more than 200 safety-related evaluations that have been applied to generative AI systems. We categorise each evaluation along multiple axes to create a detailed snapshot of the safety evaluation landscape to date. We release this data for researchers and AI safety practitioners (https://bitly.ws/3hUzu). Analysing the current safety evaluation landscape reveals three systemic ”evaluation gaps”. First, a ”modality gap” emerges as few safety evaluations exist for non-text modalities. Second, a ”risk coverage gap” arises as evaluations for several ethical and social risks are simply lacking. Third, a ”context gap” arises as most safety evaluations are model-centric and fail to take into account the broader context in which AI systems operate. Devising next steps for safety practitioners based on these findings, we present tactical ”low-hanging fruit” steps towards closing the identified evaluation gaps and their limitations. We close by discussing the role and limitations of safety evaluation to ensure the safety of generative AI systems.
The Impact of Responsible AI Research on Innovation and Development
Authors: Septiandri Ali Akbar , Constantinides Marios , Quercia Daniele
Published: November 2024

Translational research, especially in the fast-evolving field of Artificial Intelligence (AI), is key to converting scientific findings into practical innovations. In Responsible AI (RAI) research, translational impact is often viewed through various pathways, including research papers, blogs, news articles, and the drafting of forthcoming AI legislation (e.g., the EU AI Act). However, the real-world impact of RAI research remains an underexplored area. Our study aims to capture it through two pathways: patents and code repositories, both of which provide a rich and structured source of data. Using a dataset of 200,000 papers from 1980 to 2022 in AI and related fields, including Computer Vision, Natural Language Processing, and Human-Computer Interaction, we developed a Sentence-Transformers Deep Learning framework to identify RAI papers. This framework calculates the semantic similarity between paper abstracts and a set of RAI keywords, which are derived from the NIST's AI Risk Management Framework; a framework that aims to enhance trustworthiness considerations in the design, development, use, and evaluation of AI products, services, and systems. We identified 1,747 RAI papers published in top venues such as CHI, CSCW, NeurIPS, FAccT, and AIES between 2015 and 2022. By analyzing these papers, we found that a small subset that goes into patents or repositories is highly cited, with the translational process taking between 1 year for repositories and up to 8 years for patents. Interestingly, impactful RAI research is not limited to top U.S. institutions, but significant contributions come from European and Asian institutions. Finally, the multidisciplinary nature of RAI papers, often incorporating knowledge from diverse fields of expertise, was evident as these papers tend to build on unconventional combinations of prior knowledge.
Individual Fairness in Graphs Using Local and Global Structural Information
Authors: Sium Yonas , Li Qi , Varshney Kush R.
Published: November 2024

Graph neural networks are powerful graph representation learners in which node representations are highly influenced by features of neighboring nodes. Prior work on individual fairness in graphs has focused only on node features rather than structural issues. However, from the perspective of fairness in high-stakes applications, structural fairness is also important, and the learned representations may be systematically and undesirably biased against unprivileged individuals due to a lack of structural awareness in the learning process. In this work, we propose a pre-processing bias mitigation approach for individual fairness that gives importance to local and global structural features. We mitigate the local structure discrepancy of the graph embedding via a locally fair PageRank method. We address the global structure disproportion between pairs of nodes by introducing truncated singular value decomposition-based pairwise node similarities. Empirically, the proposed pre-processed fair structural features have superior performance in individual fairness metrics compared to the state-of-the-art methods while maintaining prediction performance.
Strategies for Increasing Corporate Responsible AI Prioritization
Authors: Wang Angelina , Datta Teresa , Dickerson John P.
Published: November 2024

Responsible artificial intelligence (RAI) is increasingly recognized as a critical concern. However, the level of corporate RAI prioritization has not kept pace. In this work, we conduct 16 semi-structured interviews with practitioners to investigate what has historically motivated companies to increase the prioritization of RAI. What emerges is a complex story of conflicting and varied factors, but we bring structure to the narrative by highlighting the different strategies available to employ, and point to the actors with access to each. While there are no guaranteed steps for increasing RAI prioritization, we paint the current landscape of motivators so that practitioners can learn from each other, and put forth our own selection of promising directions forward.
Dataset Scale and Societal Consistency Mediate Facial Impression Bias in Vision-Language AI
Authors: Wolfe Robert , Dangol Aayushi , Hiniker Alexis , Howe Bill
Published: November 2024

Multimodal AI models capable of associating images and text hold promise for numerous domains, ranging from automated image captioning to accessibility applications for blind and low-vision users. However, uncertainty about bias has in some cases limited their adoption and availability. In the present work, we study 43 CLIP vision-language models to determine whether they learn human-like facial impression biases, and we find evidence that such biases are reflected across three distinct CLIP model families. We show for the first time that the the degree to which a bias is shared across a society predicts the degree to which it is reflected in a CLIP model. Human-like impressions of visually unobservable attributes, like trustworthiness and sexuality, emerge only in models trained on the largest dataset, indicating that a better fit to uncurated cultural data results in the reproduction of increasingly subtle social biases. Moreover, we use a hierarchical clustering approach to show that dataset size predicts the extent to which the underlying structure of facial impression bias resembles that of facial impression bias in humans. Finally, we show that Stable Diffusion models employing CLIP as a text encoder learn facial impression biases, and that these biases intersect with racial biases in Stable Diffusion XL-Turbo. While pretrained CLIP models may prove useful for scientific studies of bias, they will also require significant dataset curation when intended for use as general-purpose models in a zero-shot setting.
Tracing the Evolution of Information Transparency for OpenAI’s GPT Models through a Biographical Approach
Authors: Xu Zhihan , Mustafaraj Eni
Published: November 2024

Information transparency, the open disclosure of information about models, is crucial for proactively evaluating the potential societal harm of large language models (LLMs) and developing effective risk mitigation measures. Adapting the biographies of artifacts and practices (BOAP) method from science and technology studies, this study analyzes the evolution of information transparency within OpenAI’s Generative Pre-trained Transformers (GPT) model reports and usage policies from its inception in 2018 to GPT-4, one of today’s most capable LLMs. To assess the breadth and depth of transparency practices, we develop a 9-dimensional, 3-level analytical framework to evaluate the comprehensiveness and accessibility of information disclosed to various stakeholders. Findings suggest that while model limitations and downstream usages are increasingly clarified, model development processes have become more opaque. Transparency remains minimal in certain aspects, such as model explainability and real-world evidence of LLM impacts, and the discussions on safety measures such as technical interventions and regulation pipelines lack in-depth details. The findings emphasize the need for enhanced transparency to foster accountability and ensure responsible technological innovations.
Ontology of Belief Diversity: A Community-Based Epistemological Approach
Authors: Zhang Richard , Van Liemt Erin , Fischella Tyler
Published: November 2024

AI applications across classification, fairness, and human interaction often implicitly require ontologies of social concepts. Constructing these well – especially when there are many relevant categories – is a controversial task but is crucial for achieving meaningful inclusivity. Here, we focus on developing a pragmatic ontology of belief systems, which isa complex and often controversial space. By iterating on our community-based design until mutual agreement is reached, we found that epistemological methods were best for categorizing the fundamental ways beliefs differ, maximally respecting our principles of inclusivity and brevity. We demonstrate our methodology’s utility and interpretability via user studies in term annotation and sentiment analysis experiments for belief fairness in language models
NarrativeGenie: Generating Narrative Beats and Dynamic Storytelling with Large Language Models
Authors: Kumaran Vikram , Rowe Jonathan , Lester James
Published: November 2024

Interactive narrative in games utilize a combination of dynamic adaptability and predefined story elements to support player agency and enhance player engagement. However, crafting such narratives requires significant manual authoring and coding effort to translate scripts to playable game levels. Advances in pretrained large language models (LLMs) have introduced the opportunity to procedurally generate narratives. This paper presents NarrativeGenie, a framework to generate narrative beats as a cohesive, partially ordered sequence of events that shapes narrative progressions from brief natural language instructions. By leveraging LLMs for reasoning and generation, NarrativeGenie, translates a designer’s story overview into a partially ordered event graph to enable player-driven narrative beat sequencing. Our findings indicate that NarrativeGenie can provide an easy and effective way for designers to generate an interactive game episode with narrative events that align with the intended story arc while at the same time granting players agency in their game experience. We extend our framework to dynamically direct the narrative flow by adapting real-time narrative interactions based on the current game state and player actions. Results demonstrate that NarrativeGenie generates narratives that are coherent and aligned with the designer’s vision.
The Harmony Index: Evaluating, Predicting, and Visualizing Effectiveness in Multi-Agent Team Dynamics
Authors: Roman Darryl , Ari Noah , Mell Johnathan
Published: November 2024

Team-based games are a keystone pillar of the gaming industry. Sadly, the understanding of team dynamics—and the recommendations for both human and AI-based teammates—are based on a rudimentary understanding of human-AI teaming. We propose a superior metric, which provides information about team effectiveness in an efficient and easily-replicable manner. Without an accurate and effective metric for team evaluation, it is nigh-impossible to provide feedback to players and game designers to improve team balance. We provide such a metric. The Harmony Index, a novel algorithm using real-world data, provides simpler and more accurate actionable directives to improve game design across MOBAs and other game genres. We prove its predictive power in a separate analysis and make recommendations for its use in assessing team effectiveness as well as its future use in additional domains.
Enhancing Two-Player Performance Through Single-Player Knowledge Transfer: An Empirical Study on Atari 2600 Games
Authors: Saadat Kimiya , Zhao Richard
Published: November 2024

Playing two-player games using reinforcement learning and self-play can be challenging due to the complexity of two-player environments and the possible instability in the training process. We propose that a reinforcement learning algorithm can train more efficiently and achieve improved performance in a two-player game if it leverages the knowledge from the single-player version of the same game. This study examines the proposed idea in ten different Atari 2600 environments using the Atari 2600 RAM as the input state. We discuss the advantages of using transfer learning from a single-player training process over training in a two-player setting from scratch, and demonstrate our results in a few measures such as training time and average total reward. We also discuss a method of calculating RAM complexity and its relationship to performance.
Generalized Entropy and Solution Information for Measuring Puzzle Difficulty
Authors: Shen Junwen , Sturtevant Nathan R.
Published: November 2024

Metrics for problem difficulty are used by many puzzle generation algorithms, as well as by adaptive algorithms that want to provide players with the puzzles at the correct level of difficulty. A recently proposed general metric, puzzle entropy, combines an analysis of game mechanics with a model of player knowledge in the form of inference rules to predict problem difficulty. The entropy of a puzzle is the amount of information required, given a player’s knowledge about the puzzle, to describe a solution to a puzzle. This paper generalizes the concepts of puzzle entropy and solution information, providing a better foundation for the previous work and creating new algorithms, Minimum Solution Information and Total Solution Information. While functionally very similar to past work, the new algorithm allows knowledge about a puzzle to be represented as a policy, something that can be learned more easily. We then evaluate the impact of policies, inference rules, and player knowledge in the 2016 game, The Witness.
Using Natural Language to Improve Hierarchical Reinforcement Learning in Games
Authors: Mobley Dave , Corwin Adrienne , Harrison Brent
Published: November 2024

This work investigates how natural language task descriptions can accelerate reinforcement learning in games. Recognizing that human descriptions often imply a hierarchical task structure, we propose a method to extract this hierarchy and convert it into "options" – policies for solving subtasks. These options are generated by grounding natural language descriptions into environment states, which are then used as task boundaries to learn option policies either by leveraging prior successful traces or from human created walkthroughs. We evaluate our approach in both a simpler grid-world environment and the more complex text-based game Zork, comparing option-based agents against standard Q-learning and random agents. Our results demonstrate the effectiveness of incorporating natural language task knowledge for faster and more efficient reinforcement learning across different environments and Q-learning algorithms, including tabular Q-learning and Deep Q-Networks.
Abstraction and Path Computation for Video Game Path Finding with Changing Maps
Authors: Saller Teresa , Lawrence Ramon , Bulitko Vadim
Published: November 2024

Efficient grid-based path finding is important in video games especially for larger maps and when moving many agents. Algorithms based on abstraction have an order of magnitude faster search time performance than A* at the cost of a small amount of memory and increased suboptimality. Some approaches also compute and store paths to improve search performance. This paper evaluates new and existing algorithm variants for abstraction and path computation and investigates their performance for video game path finding with map changes. The results show that abstraction has significant advantages over A* and can be implemented efficiently for changing maps. Computing, storing, and reusing paths also has benefits especially when several searches can be performed before the map changes.
Evaluating the Effects of AI Directors for Quest Selection
Authors: Yu Kristen K. , Guzdial Matthew , Sturtevant Nathan R.
Published: November 2024

Modern commercial games are designed for mass appeal, not for individual players, but there is a unique opportunity in video games to better fit the individual through adapting elements in games. In this paper, we focus on AI Directors, systems which can dynamically modify a game, that attempt to personalize the player experience to a player's preference. AI Directors have in the past provided inconclusive results, so their effect on player experience is currently unknown. In this paper, we take three AI Directors and directly compare them in a human subject study to test their effectiveness on quest selection. Our results show that a non-random AI Director provides a better player experience than a random AI Director.
The FarmQuest Player Telemetry Dataset: Playthrough Data of a Cozy Farming Game
Authors: Yu Kristen K. , Guzdial Matthew , Sturtevant Nathan R.
Published: November 2024

Open datasets are the foundation of many types of academic research. One field that requires datasets is data-driven player modeling, but there is a lack of variety in existing datasets. We introduce the FarmQuest Player Telemetry (FPT) dataset, a new playthrough dataset of a cozy farming game. We envision this dataset will be used for reproducing prior results, evaluating initial ideas, and evaluating the generalizability of new and existing algorithms.
Descriptive Evaluation in Outpatient Follow-up of Direct LDL-C in Patient with Elevated Triglycerides and Diabetes
Authors: Bennett Monica , Buzzard Lyndsey , Presnell Erica , Ofei-Dodoo Samuel , Newell Bradley
Published: November 2024

Introduction. An annual fasting lipid panel (FLP) is recommended for patients with diabetes, and more frequent testing is advised when escalating cholesterol-lowering therapy. However, the calculated low-density lipoprotein cholesterol (LDL-C) using the Friedewald equation may be inaccurate when triglycerides (TG) are ≥400 mg/dL. In such cases, providers are required to order a separate direct LDL-C assay to confirm results, which increases the risk of overlooking the need for therapy intensification.
Methods. The authors conducted a retrospective chart review across 13 outpatient clinics within a single health system over a five-year period. Patients aged 40-75 years, diagnosed with diabetes, and having at least one invalid LDL-C result were included. The primary outcome assessed the frequency of ordering a direct LDL-C assay within 7 days following an invalid LDL-C.
Results. Among 1364 unique cases with invalid FLPs, 97 (7.1%) met the primary outcome. No significant association was identified between provider type and the likelihood of ordering a direct LDL-C. Therapy escalation was not significantly impacted by a direct LDL-C (14.1% direct vs 16.3% indirect) (p=0.6269) or provider type (16% MD/DO, 14.9% PA, 20.4% APRN) (p=0.6871).
Conclusions: Our findings indicate that the current practice of manually verifying invalid LDL-C results at this institution may overlook necessary therapeutic intensification to meet guideline-based goals. The addition of a reflex direct LDL-C assay may enhance this process.
Type 1 Diabetes Mellitus in Movies: The Hollywood Effect
Authors: Sandid Lana , Kallail Ken James , Moore Justin , Ablah Elizabeth
Published: November 2024

Introduction. Type 1 diabetes mellitus (T1D) is one of the most common chronic diseases with childhood onset. Cinematic films and movies have the capability to reach populations worldwide and effect their concept of this disease. This research examined the accuracy of these movies depicting T1D from childhood into adulthood.
Methods. We conducted an internet search of several databases that resulted in a list of 39 movies from 2000 to 2022 with characters that had diabetes. We ultimately assessed 13 fictional movies that dealt with management of disease, character development, access to care, and complications. 
Results. Movies portray severe but rare diabetes manifestations such as comas. They emphasize access to basic diabetes supplies and cost of care. Only starting in 2020, a continuous glucose monitor (CGM) and insulin pumps are shown. Female characters are presented as resilient and unaffected by the struggles of their T1D.
Conclusions. This sample of fictional movies portrays extreme T1D symptoms and mostly outdated technology for monitoring and treatment. It would be beneficial if future movies reflected the advances in closed-loop CGM/insulin pumps. It is important for clinicians to be aware of how the movie industry is presenting the disease to their patients. Clinicians can use popular movies to start difficult discussions with their patients about topics pertinent to the comprehensive care of T1D.
Suicide Prevention across the Community: Evaluation of Mental Health Training for Multiple Gatekeeper Groups
Authors: Hawley Suzanne , Skinner Thomas , Young Marci , St.Romain Theresa , Provines Jessica
Published: November 2024

Introduction. The USA experiences higher suicide rates than the global average, with rural rates even higher. The present study examined whether a single suicide prevention training could increase knowledge, awareness, and intention to act among multiple gatekeeper populations in Kansas, a rural state with elevated suicide rates.
Methods. An evidence-based suicide prevention training program was developed by licensed clinical psychologists at a public university in Kansas and offered online to multiple recruitment groups: university faculty/staff and students, healthcare workers, and other community members on a voluntary basis; high school staff and students on a compulsory basis. Participants reported knowledge, awareness, and intention on a Likert-type scale and indicated whether they had completed previous suicide prevention training. 865 participants provided retrospective pre/post results. Paired samples t-tests were run on the full set and each recruitment group. Repeated measures ANOVAs compared results by recruitment group and previously trained status.
Results. Overall, by recruitment group, and by previously trained status, participants demonstrated statistically significant pre/post increases on all items. ANOVA found no significant differences between recruitment groups. Participants with previous training reported less change in scores but still increased scores significantly.
Conclusions. Findings support the efficacy of using a single suicide prevention training across multiple populations. This has implications for allotting limited resources in an environment of high need.
La ética como principio de vida: Un estudio en docentes universitarios
Authors:   Universidad de Guayaquil, Ecuador, Mendieta Toledo Lenin
Published: November 2024

El estudio “Relatos de vida de profesores universitarios. Una aproximación biográfica y narrativa sobre los valores, ética y moral en la enseñanza de los docentes en la universidad de Guayaquil” emerge de la investigación doctoral El perfil epistemológico del docente universitario. En ese estudio los profesores participantes de la investigación, todos ellos de la Universidad de Guayaquil, emitían una contundente conclusión: “Los valores, ética y moral están en un nivel superior que la formación profesional de quien ejerce la docencia” (Mendieta Toledo, 2023, s/p). Ante esta premisa y tomando en cuenta que el estudio doctoral fue un entramado entre formación docente, sistema de creencias y valores, decidimos indagar sobre los valores, específicamente sobre la ética de aquellos profesores que los estudiantes consideran memorables.
Palatal coarticulation in the Altai language according to ultrasound data in a dynamic aspect
Authors:   Institute of Philology of the Siberian Branch of the Russian Academy of Sciences, Timkin Timofey V.
Published: November 2024

The paper explores the movement of the tongue dorsum in phonetic words of the Altai language. The data were collected using Articulate Instruments equipment and Articulate Assistant Advanced software, employing the ultrasound imaging technique. The focus was on several monosyllabic lexemes with a “plosive vowel nasal” structure. Every word was recorded three times in an isolated context. The audio recording, synchronized in time with the ultra-sound image, was segmented and annotated based on auditory and acoustic analysis in the Praat program. The statistical analysis was performed using the R programming language. The dorsum movement was represented as a series of static frames at 10 ms intervals. The time-mapping approach was used to calculate the coefficients capturing the dorsum uplift at different time intervals, with time dynamics described as a bundle of contours for each phonetic word. The point coefficient was determined based on the highest dorsum point, indicating the elevated section of the dorsum. The height coefficient was calculated as a vertical coordinate of the highest point. These coefficients were used to determine dorsum distance from the articulation center, identify vowel row and openness, and recognize basic and additional palatal or velar articulation in consonants. The study found that in soft-type words, the dorsum uplift was consistent throughout the word, resulting in palatalized consonants. In hard-type words with dorsal consonants, the dorsum raise was limited to the beginning of the following vowel and then lowered, indicating coarticulation movement. The paper describes this process in terms of structural phonology and gestural theory.
Semantics of verbs of object transfer in Khakass and Altai languages
Authors:   Institute of Philology of the Siberian Branch of the Russian Academy of Sciences, Shagdurova Olga Yu. , Tyuntesheva Elena V. ,   Institute of Philology of the Siberian Branch of the Russian Academy of Sciences
Published: November 2024

The paper deals with the semantic features of verbs of object transfer in Khakass and Altaic languages. The use of lexemes describing the situation of physical transfer with subjects and objects of different types is analyzed. An attempt has been made to systematize verbs by components incorporated into semantics. All these verbs include obligatory signs characteristic of the situation of transfer (semantics of transfer, starting and ending points, object and subject of action). Besides, these tokens contain additional components incorporated into their semantics: “the path,” “route,” “manner of transfer transfer,” “means of transfer” (“transport” and “tool”), and “object.” According to the typological classification developed by L. Talmi, the Turkic languages belong to the verbal type, with their verb roots expressing an indication of the “the path.” The verbs with an incorporated “manner of transfer” component can have their “direction” parameter specified with satellites. The subject of the verbs under study can be a person, less often an animal, or a technical means. When secondary meanings of physical action are expressed, the subject can be expressed by natural phenomena and objects (wind, river, waves). The objects of physical movement are represented by various physical objects and living beings. The examined languages exhibit considerable resemblance in terms of the composition and semantic structures of the verbs associated with transfer. Each language has its own set of verbs used in everyday life and work activities of specific ethnic and linguistic groups.
Lyrical poetics of the novel “The Life of Arseniev” by Ivan Bunin
Authors:   Institute of Philology of the Siberian Branch of the Russian Academy of Sciences, Kapinos Elena V.
Published: November 2024

The paper examines such features of the lyrical poetics of the novel “The Life of Arseniev” in terms of its thematic and motivic repetitions, fragmentary and variegated style, and overall sense of ambivalence and uncertainty. An emphasis is placed on how various settings are described, including manor and urban locales and on the punctuated lyrical narrative of the Fifth Book of the novel. Ivan Bunin depicts several manors near Arseniev’s residence characterized by their similarities and significant historical connections to renowned Russian writers. The lyricism of the manor theme is enhanced by various variations, lending it a substantial semantic richness. The transitions between different spaces, the layered descriptions, and the concentration of similar plots within each chapter are all points of interest. The urban space is built under the same laws as the manor. The technique used to depict cities entails the repetition of similar characteristics, with the images of the provincial city of Orel gradually merging with those of an unnamed county town in Book Five. The lyrical plot of Book Five is structured around the multiplication of similar situations, with several climaxes. The spatial and plot structures exhibit duplication, complementarity, and induction elements. The multiplication of similar motifs and plot situations skillfully confuses the reader, introducing a subject ambiguity underlying the lyrical poetics of the novel. The lyrical poetics of the novel shares similarities with the classical Russian elegy, including specific details and a melancholic tone.
Representation of the Ethnicity of Small Indigenous Peoples. Introduction to the Special Topic of the Issue
Authors:   Peter the Great Museum of Anthropology and Ethnography (Kunstkamera) of the RAS, Belorussova Svetlana Yu.
Published: November 2024

The indigenous peoples of Russia exist as a separate category according to the official list, which includes 47 peoples. The indigenous people of small numbers are preserved not only due to the traditional culture inherited from their ancestors and the connection with the ancestral territory of their formation, but also due to the established relations with the surrounding peoples. This thematic section includes a study of the processes and strategies for transforming the lives of indigenous peoples. How does the image of indigenous peoples change over time? How does cyberreality merge with reality, and cyberethnicity become an integral part of modern ethnicity? How do habitual patterns of behavior and activity change, and does locality connect with globality? The selection of articles has a wide thematic and temporal coverage from the study of the perception of ethnicity in the 1920s to today's realities. The authors studied the strategies of representation of ethnicity in the 1920s — 1930s, as well as modern ethnic processes, including virtual interaction. The papers use various tools to analyze modern forms of ethnicity, including cartography and cyberethnography. The topic of the recently published results of the All-Russian Census of the Population among Small Indigenous Peoples is touched upon in the section, which actualizes and unites the experiences of positioning identity among ethnic communities. We hope that these papers will help to see new perspectives on the ethnic identity of small indigenous peoples, and new methods and broad thematic and time frames will enable a comprehensive analysis. The research results will help to expand the topic, update it and emphasize the current realities. Meanwhile, indigenous minorities today are characterized by adaptability, plasticity and strategic thinking, and the study of their resource potential is the task of modern researchers.
Indicadores de Sustentabilidade para Gestão da Água
Authors: Lima Junior Abmael de Sousa , Silva Simone Rosa Da , Alcoforado Roberta de Melo Guedes
Published: November 2024

Water safety involves the sustainable use and protection of water sources, so that water is available in adequate quantity and quality to health, livelihoods, ecosystems and production, in addition to protecting it from the negative effects of extreme weather events. Internationally, the concept of water security began being discussed in the 1990s, and more so since 2009, when the issue became the subject of a greater number of publications. In Brazil Federal Law 9433/97, which establishes the National Water Resources Policy, and corresponding state laws implicitly provide the concept of water security. However, the National Water Safety Plan was only launched in 2019, and has became the fundamental instrument for decision-making on this issue. The objective of the research was to pre-select indicators to analyze the water safety of the municipalities contemplated in the Capibaribe river watershed, within the framework of sustainable water management. To achieve the objective, the following methodology was adopted: pre-selection of indicators; application of a questionnaire to a panel of experts; structure a database. Thus, this work aims to bring subsidies that contribute to water security actions and public policies through the development of an indicator database. Additionally, this research is a proposal for future work, helping to define an equation of the water security index for the municipalities in the watershed.
O trabalho na Educação Ambiental Crítica
Authors: Ribeiro Mariana Alves , Queixas Ricardo Campos , Festozo Marina Battistetti
Published: November 2024

An environmental education (EE) system that does not analyze social and political issues cannot contribute to understanding the roots of problems involving the environment. The study of social relations and the production of life through work is fundamental to understanding the historical connection that human beings establish with nature and to pointing out ways to confront the environmental crisis - a symptom of a broader, civilizing crisis. More specifically in teacher training programs – essential subjects to the EE approach at school – it is important to not be fragmented and disconnected from the reality into which teachers and students are immersed. Therefore, analyzing and understanding the historical relationship between school, community, and their surrounding environment, especially regarding the concept of labor, is one of our bets for the training of critical and engaged teachers and citizens. In view of these concerns, we aim at describing and analyzing part of a teacher training program (initial and continuing) for members of a Pedagogical Residency Project, which occurred through the elaboration and carrying out of a participatory research study – an environmental mapping – with the community surrounding a local partnering school in Lavras-MG. Through this participatory research, content analysis, and the interviews with community members, we observed that the study of working relations, as well as other particularities of the local community, can bring elements of great relevance to the exercise of critical EE, especially in this context of teacher training.
Inventário e Análise da Arborização de praças no município de Campo Belo, MG
Authors: Neto Antonio Vieira , Sousa Kelly Iapuque Rodrigues de , Borges Luís Antônio Coimbra
Published: November 2024

Due to the population growth in urban areas, it is necessary to rethink city planning. Parks, as accessible green spaces, play an important role in providing leisure, social interaction, education, and ecosystem services, contributing to improving quality of life.he objective was to carry out a qualitative and quantitative inventory of trees located in Cônego Ulisses and Menotti d’Áurea squares in Campo Belo, Minas Gerais. On-site data collection included identification, measurement (DBH, height, and cankers), and visual assessment (inadequate pruning, mechanical damage, apparent nutritional deficit, and pests). By cross-referencing the data, a classification of the trees' phytosanitary status was conducted. A total of 257 trees were found, with 70% belonging to native species and classified as large-sized. Only 3.5% of the trees showed apparent nutritional deficits. Regarding pests, 4.66% suffered attacks by termites, 5.44% by borers, and two individuals were attacked by caterpillars. It was observed that 10.89% had larger canker dimensions. It was noted that 43.97% of the trees were pruned irregularly, outside of the recommended technical standards, and three species suffered mechanical damage due to vandalism. It was identified that 52% of the trees classified under criterion III (precarious tree) were Pau Ferro. The inventory in the squares showed a predominance of native species, but planning for the planting of new specimens should respect specific diversity criteria. Monitoring needs to be carried out, and replanting planning should prioritize the replacement of precarious trees. Training should be provided for professionals performing pruning, and environmental education strategies should be developed for the population.
Study on the mechanism of hyperoside in affecting the biological progression and radiosensitivity of esophageal carcinoma by modulating the STAT3/AKT/ERK pathway
Authors: Yin Hongmei , Yuan Zhongxia , Han Xiumei , Jiang Die , Li Duojie , Song FengLi
Published: November 2024

Hyperoside (HYP) exhibits diverse pharmacological effects and holds potential for enhancing chemotherapy sensitivity. However, few studies have reported the impact of HYP on the malignant progression of esophageal carcinoma (EC) and its sensitivity to radiotherapy. The impact of HYP on the viability of EC cells (TE-1 and KYSE-150) was assessed using Cell Counting Kit-8 (CCK-8) assays. The biological characteristics and radiosensitivity of EC cells following HYP treatment were evaluated through clone formation experiments, flow cytometry, scratch wound-healing assays, and transwell migration and invasion assays. Western blot analysis was performed to determine the levels of proteins associated with cell death and epithelial-mesenchymal transition (EMT), as well as to explore whether HYP interferes with the radiosensitivity of EC cells via the  STAT3/AKT/ERK pathways. Finally, a subcutaneous graft tumor model was constructed to investigate the effects of HYP and X-ray treatments on in vivo tumor growth. The findings indicated a dose-dependent decrease in the survival rate of KYSE-150 and TE-1 cells following HYP treatment. HYP treatment also inhibited cell proliferation, invasion, migration, and EMT, while increasing the apoptotic rate and radiosensitivity of the cells. Notably, HYP suppressed the malignant progression of EC and enhanced radiosensitivity via the STAT3/AKT/ERK pathway. Moreover, HYP impaired the growth of EC tumors in mice, with the combined HYP and X-ray treatment exerting a stronger inhibitory effect. In conclusion, HYP increases the radiosensitivity of esophageal carcinoma cells, offering considerable promise for application in the clinical treatment of EC.