dataesr/scientific-paragraphs-categorization huggingface dataset:
Transformational adaptation in islands Paradoxically, while island representatives usually describe climate change as an "existential threat" involving unprecedented challenges to development and historical socioeconomic patterns, the scientific literature remains sparse on discussing what transformational adaptation (could) look like in various island contexts (e. g. , relocation of settlements and infrastructure or changes in livelihoods)  (Nunn and McNamara, 2019). The review of the GAMI database for evidence on transformational adaptation on islands resulted in six articles coded as adaptation with high "depth" and accordingly no significant patterns (see Supplementary Table  SM1. 1 and Supplementary Figure SM1. 4 ). Yet three of those were excluded.
, 2019). While this review builds on the most comprehensive database of climate change adaptation literature, it is limited in terms of time span covered  (2013) (2014) (2015) (2016) (2017) (2018) (2019). It therefore misses potential evidence on island adaptation and transformation from past/historical experiences, as well as relevant evidence published in non-English languages. Many other languages do not have terms or phrases that directly mean "adaptation" or "transformation. " The database, therefore, suffers from and perpetuates "Anglophone squint"  (Aalbers, 2004). One consequence is that cultural or world view lenses that are seen as requiring an adaptation-or transformationrelated response would be excluded, not merely because they do not use these specific words, but also because the notion might be absent from their understandings of the world.
Even in science in English, not all disciplines or authors adopt climate change jargon, such as adaptation and transformation, even though their work covers similar ideas and actions. The bias in GAMI including the "Anglophone squint" could explain the earlier mentioned result about the under-representation of different knowledges within the papers. Coverage of further scientific databases, vocabulary, gray literature and non-English language literature may help fill the gaps in recent assessments and reviews  (Haddaway and Bayliss, 2015; Chowdhury et al. , 2022). By relying mainly on the GAMI evidence for identifying potentially transformational adaptation, we limit ourselves to the rather generalized coding of low-to-high depth, without further specifying the nature of climate change-related responses, especially in terms of transformation.
Authors calling for a benchmark approach first proposed one that compares microscopic models based on a performance metric. Vehicle count data were collected on simple single-lane networks and used as the benchmark. The chosen metric is the average error, relative to the observations, on the individual values of travel time  [18] , gaps between vehicles  [16] , or spacing, speed, and acceleration  [21]. The set of parameters of the different models is, at the same time, calibrated on the data, and the selected set is the one that optimizes the chosen metric. Such a statistical method for calibration has been used in many comparative micro-simulation tools studies for more complex and realistic cases, but not for benchmark purposes  [15, [22] [23] [24]. To our knowledge, no generic methodology for benchmarking micro-simulation tools has been developed to assess global traffic behavior.
The need for a shared macroscopic basis As three simulation software are compared, they need to be used on a common basis, i. e. parameters related to the CF models only are calibrated, and the other ones keep their default value. Variability for those critical parameters is not considered, as the analytical models used for the benchmark do not explicitly account for it; see part 3. This calibration of microscopic parameters is carried out to approach the macroscopic values of Table  1  for each simulation tool, ensuring traffic flows similarly between the three tools. Three parameters common to all the tools are of interest: the desired speed u, the minimum spatial gap g, and the minimum relative 1 headway τ. Note that in Aimsun and SUMO, the models decompose g into two terms, the vehicle length l and the relative minimum gap g min , so that g = l + g min.
We study the dynamics of interactions between a traditional medium, the New York Times journal, and its followers in Twitter, using a massive dataset. It consists of the metadata of the articles published by the journal during the first year of the COVID-19 pandemic, and the posts published in Twitter by a large set of followers of the @nytimes account along with those published by a set of followers of several other media of different kind. The dynamics of discussions held in Twitter by exclusive followers of a medium show a strong dependence on the medium they follow: the followers of @FoxNews show the highest similarity to each other and a strong differentiation of interests with the general group. Our results also reveal the difference in the attention payed to U. S.
Several works studied the evolution of the opinion in Twitter (and other platforms) during this period, mainly focusing on discussions directly related to health issues, or public policies related with them  [23] [24] [25]. Here, on the contrary, we aim at understanding how the different topics that interested the society during this period were addressed both by the media and by the public that is in direct relation to them, without assuming a priory the existence of any influence on either direction. While some recent studies have compared how traditional media and social networks treat a particular topic of discussion  [26] [27] [28] [29] [30]  , in this work we search for global patterns characterizing each of them. We have collected a large amount of tweets corresponding to a randomized sample of the over 46M followers of the New York Times (NYT) official Twitter account (@nytimes), during the first year of the pandemic, along with the metadata of the articles published by the journal during that period.
In the general context of agenda-setting, the extracted topics from a text corpus might operationalize either frames or social issues, depending on each specific context or dataset, as suggested by different works that use this approach: Danner et al. specifically study the correlation between media and public agendas related to organic food, assuming that each extracted topic is a sub-issue inside the general 'organic food' issue  31  ; Albanese et al. perform non-negative matrix factorization (NMF) of the document-term matrix to study the coverage of different issues during the 2016 US presidential campaign  32  ; Barberá et al. use LDA to study the issues discussed by members of Congress, ordinary citizens, and media outlets on Twitter, a-priori assuming that the extracted topics represent issues  33.
This will be done by analyzing global measures such as issue salience, attention diversity, rank diversity, and reaction times. By an extensive analysis of these data we aim at getting an insight into the following questions: • Who talks about what? Do people that follow a journal talk about the same subjects that are published in the journal? In that case, is it possible to quantify to what extent? • How the attention that the followers of the journal pay to different topics compares to the attention that fol- lowers of other media pay to those topics? • Can we observe any evidence about the agenda setting problem? If so, in which sense? As discussed by Scheufele et al. , agenda setting is an inherently causal theory, however, the research designs and statistical methods employed to study it are seldom suited to make causal inferences  36.
Results We collected data for over a year, starting on January 2020, before the outbreak of the pandemic, from followers of the @nytimes in Twitter, and also from Twitter users who follow Twitter accounts of other media, like @ washingtonpost, @WSJ (The Wall Street Journal), @TIME, continuous information television channels like @ FoxNews, or @CNN, and also press agencies like @AP (Associated Press) and @Reuters. During the same period, we have also collected the metadata of the publications of the NYT journal, in particular the articles' headers (see "Methods"). In order to automatically determine the topics of discussion in Twitter, we build a hashtag network where two hashtags are connected if they appear in the same tweet (see "Methods"). This link is weighted by the number of different users that used that pair of hashtags, which diminishes the potential influence of automated accounts.
@nytimes @FoxNews all 0 0. 05 0. 1 Jan Mar May J ul Sep N ov Similarity @nytimes excl. @FoxNews excl. all × @FoxNews excl. @nytimes @FoxNews all Figure  7. Dynamics of self and cross-similarities corresponding to sub-populations that follow different media accounts in Twitter. For clarity we concentrate on the curves involving the followers of @nytimes and @ FoxNews, along with a randomized sample that includes followers of all media, labelled "all" (more curves in the Supplementary Material). The labels '@nytimes excl. ' and '@FoxNews excl. ' refer to the sub-populations whose members only follow the cited medium. 'all × @FoxNews excl. ' is the cross similarity between the exclusive followers of @FoxNews in our dataset and all users (including the followers of @FoxNews) in our dataset. Top panel: Self-similarities of the different sub-populations along with the cross-similarity of exclusive followers of @FoxNews against the set of all users.
Bottom panel: Same data recomputed after the suppression of the #covid topic from the topic vectors. detection in this network. For the NYT journal, the topics are identified by the keywords chosen by the journalists to label their articles. The entropy of hashtags and keywords usages captures the structural differences among these two kind of media: the curves of the entropy of the vocabulary used by the followers of all the media in Twitter show very similar dynamics including minor details, but all of them show a dynamical behaviour that is different from that of the NYT journal. We observe that the journal is much more concerned with political news than its own followers, as shown by the sudden decrease of keyword entropy located around key political dates, for example, during the electoral period.
Methods In this section we present the data set used in this work, explaining the rationale leading to this particular choice, along with the procedure used for its collection from different data sources. We define the semantic networks built with these data and we explain how we automatically detect the set of topics under discussion and the evolution of the attention each user pays to them. Moreover we also give the mathematical definition of the observables used to characterize the dynamics of discussion in Twitter and that of the treatment of the news by the NYT over the studied period.
Data collection. Data from Twitter. We first recall briefly the standard vocabulary used to name different elements of the Twitter micro-blogging platform. Users can engage on many different levels with each other. Each user has a Twitter handle, which starts with '@'. They can write tweets, short messages consisting of up to 280 characters, which may also contain images, videos or sound, and which are shown to their followers -other users subscribing to the their accounts-on their timeline, the list of latest posted tweets. However, even non-followers can see and interact with them (except for private tweets which are not part of our dataset). Users can retweet the tweet of another user, which means that they share this tweet with all their followers. They can quote a tweet, meaning that they republish the original tweet with a comment.
• Roughly every 2 months we requested, for all users in our sample for which we already found tweets for the year 2020, the new tweets they published since our last query. Table  2  gives the main characteristics of the data used for this study. At the beginning of March 2021, we had collected up to half a billion tweets published by more than 8M (8,151,587) followers. As it is well known that only a minority of Twitter users include their geolocalization in their profile, we have chosen not to control for this variable so as to avoid artificially diminishing the number of collected users. However, since the US is the largest market both for Twitter and NYT, we expect that most followers are indeed located in US. As a consequence, although we cannot rule out that the dataset contains tweets of users living abroad, we will naturally focus on events that are relevant to the US in order to tag the chronology of the study.
The pertinence of this choice is supported by the fact that topics which are popular in the US are dominating the discussion, and we show that it is possible to identify the rare exceptions. This dataset, in the form of user and tweet ids, is available at  57. Although our method enables us to collect a large sampling of a specific subpopulation of Twitter, avoiding biases that may be introduced by filtering, for example by hashtags, we discuss below some limitations that might still remain in this data set, along with an estimation of their potential influence in our study. • Due to the limit set by the API (it delivers only the last 3200 Tweets of the requested user), we risk to system- atically miss tweets of very active accounts: those who would have tweeted more than 3200 tweets between January 1st and July 1st or those who would have exceeded that limit during the ∼ 2 month period of each collection step hold after July 1st 2020 until the end.
This means that the problem of missing tweets from very active accounts is worse for this data set. However, the fraction of incomplete accounts remains small < 0. 3% (even smaller than for the @nytimes dataset, because we only had one cycle causing fewer false positives). Again, this dataset in the form of user and tweet ids is available at  58. Finally, we also collected all tweets of the @nytimes account for the period, referenced by retweets, quotes or replies of their followers. In this study we only use metadata of the tweets: hashtags normalized to lower case (i. e. , we treat #covid-19 and #COVID-19 as the same hashtag) and URLs. We do not extract further data from the remainder of the tweet, neither text nor images nor videos. Nevertheless, we will show that this minimalist information contained in the tweets already provides a rich image of the public discussion in the platform.
Data from the NYT. Table  4  describes the main figures involved in the analysis of the publications of the NYT journal during the same period. In addition to the data from Twitter users, we collected the metadata of all articles published by the NYT either in print or online using their archive API. This dataset includes in particular, a set of keywords for each article, which lists subjects, persons and locations referred to in the article. Moreover, it provides unique identifiers, Observables. We detail in this section the quantities or observables that we used in this study. Entropy. The entropy of the hashtag distribution over a timeframe t is defined as: where p t (i) is the probability distribution calculated as the ratio between the number of unique users that have used hashtag i and the number of different pairs (hashtag, user) within the time frame t.
However, multiple hashtags may address the same topic. A common strategy to follow the discussion about a topic is to pre-select the hashtags that are supposed to be related to the topic. Here we use a different approach where the topics emerge from a semantic network of hashtags  37, 61. The vertices of this network are the hashtags found in our dataset, and the weighted edge between two nodes represents the number of different users that used those hashtags together in at least one of their tweets. In clear, if the same user publishes many tweets including the same pair of hashtags, it contributes to the weight only once. The rationale behind this construction is that two hashtags used in the same tweet refer to the same subject; in fact, previous work has shown that hashtag co-occurrences in tweets are mostly coupled with semantic relations  62.
Acknowledgements The authors acknowledge the OpLaDyn grant obtained in the 4th round of the  Trans-Atlantic Platform Digging into Data Challenge  ( 2016-147 ANR OPLADYN TAP-DD2016 ). H. S. acknowledges grant  Labex MME-DII  (Grant No. ANR  reference  11-LABEX-0023 ). J. I. A. H. and M. G. B. acknowledge the financial support of  UBACyT -2018 20020170100421BA and the OpLaDyn grant  HJ-253570 Annex IF-2017-14123506-APN-DNCEII#MCT.
In doing so, it will be necessary to take a research approach that accounts for the diversity of regulative and socio-economic settings. We illustrate our arguments with interview data collected from Finnish managers from the food packing industry. However, supererogatory management as an new management ethos will take different forms in different settings and we expect diverging prerequisites, opportunities and threats in each of these settings. Finally, we are certainly not advocating replacing traditional management with supererogatory management but see the two forms as complementary when venturing into an unknown future, aligned with Hakala and Vuorinen (2020) who suggest extending the toolbox of strategic decision making to include empathy, quasi-rationality and responsibility.
Acknowledgements The authors wish to acknowledge financial support from the research projects " Reasons and Emotions " funded by the  Academy of Finland  (grant no. 307597 ), " Package-Heroes " (grant no. 320217  and  346598 ) funded by the  Strategic Research Council at the Academy of Finland , " Emotion as ethical compass in new venture creation " funded by The  Foundation for Economic Education  (grant no. 12-6783 ), and " The role of emotion in responsible innovation " (grant no. 18-10616 ) funded by  The Foundation for Economic Education.
This is especially given the data collection and analyses testing our setup to check her assumptions and recommendation, this is truly extraordinary. Her insights are inline with our experience regarding such matters. We note that in the process of addressing the way to aggregate insights and writing the mini meta-analytic code we realized minor gaps in our planned reporting in the methods section (regarding versions 1-4 in Study 4) and amended the tables and our R code accordingly. In addition, we added planned reporting of several measures we considered exploratory that were included in the target's data collection but not reported, which we included for the sake of a comprehensive replication and will analyze in our design and will report in the manuscript. Reply to Reviewer #1: Dr. /Prof.
We contemplated many different ways to approach this, but at the end, we chose to address this by (mini) meta-analyzing the effects within each hypothesis, first collapsing the within-effects, and then aggregating the different samples in the four between-subject studies. Our conclusion of whether a hypothesis is supported or not would depend on the (mini) meta-analytic summary. We added the R code to compute that to the OSF (files BS2011-meta. Rmd/. html/. csv), and added the following in the "Evaluation criteria for replication findings" section: For each of the five hypotheses there are multiple data sources from different studies, and the diversification hypothesis also has three sub-hypotheses, summarized in Table  5. Each hypothesis was tested in 2-3 studies, and some of the studies with multiple versions.
c. Finally, to see for myself whether charity order might impact your results, I collected data from 399 Prolific participants using four of your scenarios: waste/overhead (study 1), diversification with equal efficiency (studies 1-2), nationalism (study 1), and average cost (study 2). I used your exact stimuli and measures, but randomized which charity was A vs. B (and thus allocations in the reverse-order condition were reverse-coded). I found a few marginally significant order effects: For example, in the average cost scenario, allocations to the lower-average-cost charity were lower when it came second (as charity B, original order) rather than first (as charity A, reverse order), t=1. 83, p=. 069. And in the overhead scenario, allocations to the low-overhead charity were somewhat lower when it came second (as charity B, original order) rather than first (as charity A, reverse order), t=1.
What you've done here is something we should all aim to learn from, keeping an open mind, thinking about and testing our assumptions. This is what we've been trying to do with our systematic replications of the judgment and decision-making literature. The very least we feel we can do here to recognize this review and contribution is that if you upload all the materials, data and code to a public OSF folder with a DOI and provide us with a link and citation, then we would be very happy to cite this in the Stage 2 manuscript together with a link to your review.
Step 2-Landscape project design In the second step, a design workshop for Thanh Niên PFG was held for one week. This design activity was carried out by three groups of architects who were part of HAU and were involved in all the activities from the beginning of the research project. Each group produced an urban landscape design proposal for the Thanh Niên PFG, based on data collected and analyzed from phase 1 and assessments during previous stakeholder meetings in step 1. Step 3-Evaluation and recommendation After the completion of three landscape design proposals, an exhibition was held to present them to the public. At the same time, a community survey was conducted in two ways: on-line and in person, with the participants at the event. The survey had three goals: (i) Collection of information on the purpose and community demand for Thanh Niên PFG space based on landscape design proposals; (ii) Evaluation and classification of three landscape design proposals; and (iii) Collection of community comments and ideas for the landscape design proposals.
PFGs Mapping Data in Five Districts of Hanoi's Historic Centre The spatial mapping activity carried out in phase 1 of the research project resulted in a set of iconographic profiles of all the PFGs in the five central districts. For the first time, a PFGs mapping database was created. It is noteworthy that the implementation of data collection in phase 1 encountered many difficulties because the number of, and information on, PFGs in the five central districts of Hanoi in the official reports was not consistent. The survey results identified a total of 30 PFGs in the five central districts (Figure  1 ), of which Ba Ðình and Ðống Ða districts have 4 PFGs each, Hai Bà Trưng and Tây Hồ districts have 5 each, and Hoàn Kiếm district has 12 PFGs (Table  A1 ). Each PFG is described by a set of iconographic documents, including maps, photographs, plans, and sections, as follows (cf.
The number of the cross-sections depends on situation of each PFG. -Street furniture at the PFG, including urban equipment such as toilets, benches, billboards, etc. , is integrally represented in the lighting system documents, depending on the situation of each PFG. A comprehensive dataset has been established, including images and detailed drawings that describe not only technical information but also the operational and functional aspects of all PFGs at different time points throughout the day. These are characteristic results of the landscape approach when using specialized landscape architecture tools, such as drawings, to describe the spatial and temporal characteristics of a PFG. This dataset has helped all stakeholders to easily understand the features, nature, and operation of the PFGs.
Sociological Survey Another result from phase 1 of the research project was a sociological dataset on 30 PFGs in five central districts of Hanoi. The responses were collected from 234 respondents, in which 45% were female and 55% were male. The ages of the respondents was mainly from 23-60 years old (52%). The groups of people aged 14-22 and over 60 years old accounted for 14% and 34% of the participants, respectively.