dataesr/scientific-paragraphs-categorization huggingface dataset:
Since its inception, GPT models have been trained using huge datasets (around 575 GB training dataset in case of GPT-3) and significant number of parameters (175 billion in case of  to be able to answer question prompts (OpenAI Research  Blog LM, 2023). However, GPT-3 would produce incorrect outputs having false information (referred to as hallucinations), which may include harmful and offencive content as there were no safety parameters and may also produce irrelevant results. This led to integrating supervised and reinforcement ML techniques to incorporate human feedback in the training process which will produce outputs aligned to the users' intent and context of the prompt  (Ouyang et al. , 2023).
F I G U R E 1 Evolution of GPT. 17488583, 2023, 3, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/1748-8583. 12524 by National Health And Medical Research Council, Wiley Online Library on [09/08/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Therefore, GPT-3 model was fine-tuned by hiring human contractors to create a labelled training dataset, that is, for each input (collected from past GPT-2 user prompts), contractors will provide an appropriate response, for the model to learn from. This does not, of course, preclude the possibility of biases, or incorrect choices, by the said contractors. This model became GPT3. 5. It was enhanced to better handle diverse prompts using reinforcement learning with human feedback (RLHF) technique (  (Ouyang et al.
Most notably, government of Iceland has partnered with OpenAI to use GPT-4 to preserve the Icelandic language  (OpenAI Customer Stories, 2023). Microsoft Office 365 is integrating GPT-4 to create an entire PowerPoint presentation with a single text prompt or summarize long documents in Word (Microsoft  Blog, 2023). The initial business applications of generative AI demonstrate its capabilities, but the ultimate acceptance and adoption of this technology will depend on the resolution of significant ethical and security concerns. The emergence of ChatGPT in the public domain has also sparked a surge of interest and competition in the AI chatbot market. Microsoft's announcement of a $10 billion investment in OpenAI has further amplified this trend, prompting other technology providers to enter the race.
, 2022). One of the interesting issues raised by  Renkema (2023)  refers to the question posed by  Susskind and Susskind (2022) : How is new HRM knowledge developed if HRM professionals are no longer necessary and replaced by technology? Replacement, substitution, value, imitation, and scarcity in HRM almost automatically make us think about SHRM and the resource-based view. Chaudhary (2023)  summarizes a few areas where ChatGPT can improve HR processes including improved cost-effective solutions; easier HR data management and analytics; enhanced employee experience based on survey input; and simplified recruiting and onboarding. In this overview we focus on the possible negative and positive effects of ChatGPT using the classic RBV  (Paauwe & Boselie, 2003)  and an alternative theoretical framework we call: Open resource-based view to SHRM 1.
• Outdated information: ChatGPT is built and trained on existing data. This implies that actual and future developments have not been considered. It is unclear to what extent ChatGPT and artificial intelligence in general can generate new insights and information by itself. • Bias and discrimination: Experiences from large firms, which reflect on their recruitment efforts using artificial intelligence or ChatGPT, warn for racial bias and a tendency against female candidates. • Privacy, Data security and GDPR 2 : Data shared with the chatbot could potentially be exposed to unauthorized third parties or misused, resulting in data breaches or privacy violations. • Legal and ethical concerns: What are the legal and ethical implications of using artificial intelligence chatbots? Who owns the IP of generated content? Will there be national laws or sectoral regulations, which hinder or forbid the use of ChatGPT? The summary of these potential risks can be considered 'critical conditions' for an alternative resource-based view approach to studying the impact of ChatGPT on SHRM.
• Degree to which openly available software such as ChatGPT is beneficial to all (including vulnerable employee groups, workers from ethnic minorities and computer illiterates) and not only to the privileged (the core employees or 'happy few'). • The impact of the ChatGPT -human interface on employee well-being as a form of digitalization and dehumanization of organizations and work. • Context and Uniqueness: Using openly available software such as ChatGPT, while at the same time achieving uniqueness by including contextual sectoral and organizational specific knowledge and insights.
• raise ethical and legal issues related to intellectual property, privacy, consent, covert surveillance and accountability of employees' personal data. • cause complex consent issues, for instance, due to the lack of transparency; for instance, if stakeholders were subsequently to use employees' data for reasons other than those specified in the original consent, especially if data enters an LLM's database. • not always be fully in tune with the context, intent, nuance or tone of the user or the task and therefore produce inaccurate narratives. • generate wrong, inappropriate or irrelevant responses. • create new jobs that may require high-tech skills or be in the "gig economy"; most such jobs are not currently covered by unions. • foster employees' psychological distress and decrease their sense of wellbeing.
| Diversity of perspectives Research has shown that while diversity may lead to conflicts and disagreements in the shorter-term, it can contribute to increased creativity as different perspectives (stemming from a member's gender, race/ethnicity, background and so on) can be incorporated in deliberations to arrive at a final decision. While ChatGPT has been trained on massive databases, the reality is the output reflects largely the algorithms that were written predominantly by white males. For one, AI can be racists and sexists as captured in the title of Noble's book, Algorithms of oppression: How search engines reinforce racism,  Noble (2018). For example, in early 2018, Google's facial recognition function was found to be more accurate for identifying white men but less so for women, and even less so for African Americans.
A report published by the US Department of Commerce's National Institute of Standards and Technology, prepared by  Schwartz et al. (2022) , highlights the disturbing incidence of biases against women, people with dark skin tones, older people, the disabled, and people wearing glasses. Algorithms with such built-in biases can result in discriminatory decisions pertaining to hiring/selection, promotion, firing, evaluation of performance, and compensation. In short, under the guise of science, the perpetrators of such biased decisions may defend the so-called objectivity of their actions. Aside from possible biased decisions, it is important to recognize ChatGPT is trained on large datasets. Since most datasets are in English, they can disenfranchise people whose languages are very different from English.
| Introduction The growing use of ChatGPT by organisations in an expanding range of functions and activities, including HRM functions and activities  (Howlett, 2023) , has implications for equity, diversity and inclusion (EDI), especially in employment security, recruitment and selection, performance appraisal and pay. Previous research indicates that AI can be an efficient and effective way of managing HR functions to enhance fairness. However, ChatGPT is designed to synthesise information from the database available, applying algorithms used to train it. While ChatGPT is designed to be neutral, the data it deploys to generate the texts come from a wide range of sources from the Internet, which may contain biased opinions and prejudices including, for example, racist and sexist language. Therefore, the use of AI has mixed benefits and pitfalls for organisations adopting the technology and their employees.
Adopting a critical mindset As researchers we use of a wide variety of tools such as online databases and data analysis software; but we do so with a critical mindset. We are trained, for example, to be cognoscente of the need to evaluate the validity and utility of data and to ensure the analysis techniques used are appropriate for our data. Similarly, when we read text, we need to do so with a questioning and sceptical mindset, adopting a critical approach. Drawing on  Habermas's (1984 Habermas's ( , 1992) )  theory of the validity claims of speech acts,  Mingers (2000, p. 225 ) identifies four dimensions to such an approach: • 'Critical thinking -the critique of rhetoric' questioning the logic of argument and fair use of language. • 'Being sceptical of conventional wisdom -the critique of tradition' questioning taken-for-granted assumptions about traditional ways of doing things.
Regarding ethical issues, ChatGPT provided some broad principles (e. g. , informed consent, anonymity, and confidentiality, data protection). At the measurement stage, ChatGPT can assist with selecting specific instruments. In the context of our case study, ChatGPT recommended using several scales for measuring job commitment and leadership quality: Organizational Commitment Questionnaire, Affective Commitment Scale, and Multifactor Leadership Questionnaire. Concerning the analysis stage, ChatGPT cannot yet conduct analyses  (Burger et al. , 2023). Still, it can support researchers by helping them choose appropriate statistical techniques or generate code to run specific analyses for software such as R, Python, SPSS, and SAS. Moreover, it can help regarding critical data-analytic decisions such as managing outliers and missing data and which control variables to include in the analysis.
Protection of vulnerable populations: Ensure   (Lance, 2011). Our case study illustrated several limitations of using ChatGPT as a methodology assistant, including too many suggestions, too few suggestions, and too general suggestions. First, for some questions, ChatGPT provided an abundance of responses (e. g. , more than one measure for the same construct) that could lead to decision paralysis for junior researchers who may not know which response to choose or may be overwhelmed by the number of responses provided. Second, while ChatGPT has made significant advancements in natural language processing, it still has a limited contextual understanding of problems  (Farndale et al. , 2023)  that could potentially limit the number of returned suggestions. For example, in the analysis stage, ChatGPT recommended multiple regression.
For example, to what extent does it offer useful and accurate suggestions on which specific assumptions are tenable and which specific models (e. g. , fixed vs. random effects) should be implemented? Our case study is just the beginning of a research agenda aimed at understanding generative AI's capabilities and limitations as a methodology assistant. Finally, using generative AI as a methodology assistant raises unique ethical considerations. First, publishers and journals are now reaching a consensus on how AI's writing assistance should be acknowledged in submitted manuscripts  (Eddleston et al. , 2023; Elsevier, 2023). However, can generative AI help researchers address possible ethical dilemmas and trade-offs? What choices would ChatGPT make when asked about solving ethical dilemmas? Would it embrace a value-based (i.
In this context we have a responsibility to deepen our understanding of how to promote positive outcomes of the ongoing digitalization powered by generative AI systems, while also protecting people from potential negative effects. This requires us to consider the notion of digital responsibility and how it applies to our work in varying contexts. Recently, creators of ChatGPT attempted to explain the underlying reasons for the outputs produced by a GPT2 model using GPT4, which will help users to better understand the behaviour of the model (Griffin, 2023). However, the explanations were poor, which was attributed to AI unable to explain the complex behaviour of these models in a language that humans can understand. For instance, Facebook uses human content moderators, to remove inappropriate, disrespectful, or offencive posts because AI algorithms are unable to detect and remove content that does not match the community standards  (Meta Help Center, 2023).
For instance, bibliometrics analysis has become a popular method to understand research trends by analysing bibliographic data obtained from research databases using software tools (Mukerjee et al. , 2022). However, the analysis does not automatically and without expertise and knowledge of human researchers, lead to critical insights, unearthing reason behind interesting phenomena and evolution of theoretical contributions and such is the case with generative AI. Irrespective of the preliminary guidelines and policies provided by the publishers on the use of AI tools in research (for e. g. , Elsevier, 2023), the proliferation of such tools will increase and ought to become more sophisticated. Therefore, future studies should identify the advantage and limitations of such tools, and how they can be used responsibly in academic research upholding the quality, contributions, impact and integrity of the work we publish.
Downloaded from https://onlinelibrary. wiley. com/doi/10. 1111/1748-8583. 12524 by National Health And Medical Research Council, Wiley Online Library on [09/08/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License ChatGPT can suggest effective ways to communicate results during the reporting of results stage in accordance with established standards by supplying code for generating data visualizations and recognizing implica-tions and limitations of the study. In our case study, ChatGPT provided a list of several useful suggestions consistent with reporting in terms of transparency and replicability (e. g. , describe the sample, report effect sizes) (Aguinis et al.
Powerful, workflow-agnostic and interactive visualisation is essential for the ad-hoc, human-in-the-loop workflows typical of cryo-electron tomography (cryo-ET). While several tools exist for visualisation and annotation of cryo-ET data, they are often integrated as part of monolithic processing pipelines, or focused on a specific task and offering limited reusability and extensibility. With each software suite presenting its own pros and cons and often tools tailored to address specific challenges, seamless integration between available pipelines is often a difficult task. As part of the effort to enable such flexibility and move the software ecosystem towards a more collaborative and modular approach, we developed blik, an open-source napari plugin for visualisation and annotation of cryo-ET data (source code: https://github.
com/brisvag/blik). blik offers fast, interactive, and user-friendly 3D visualisation thanks to napari, and is built with extensibility and modularity at the core. Data is handled and exposed through well-established scientific Python libraries such as numpy arrays and pandas dataframes. Reusable components (such as data structures, file read/write, and annotation tools) are developed as independent Python libraries to encourage reuse and community contribution. By easily integrating with established image analysis tools -even outside of the cryo-ET worldblik provides a versatile platform for interacting with cryo-ET data. On top of core visualisation features -interactive and simultaneous visualisation of tomograms, particle picks and segmentationsblik provides an interface for interactive tools such as manual particle picking, surface-based and filament-based particle picking and image segmentation, as well as simple filtering tools.
Effective human-in-the-loop pipelines require powerful and user-friendly visualisation tools to minimise the friction at the human-machine interface, and should be composed of modular and extensible software, to maximise reusability and simplify integration of different existing toolkits. A common practical challenge encountered by scientists working on cryo-ET data is indeed the (in)compatibility between different software tools. For example, some of the most widespread cryo-ET software suites such as IMOD  [15] , Relion  [16] , and Dynamo  [17]  all have their own file formats for particle poses and tilt series metadata. This constitutes a barrier for users who need to use features from different tools on the same data: at best, users might miss out on important features from other software; at worst, integration may silently go wrong and cause issues in later steps.
Entire software suites such as Scipion  [18]  are devoted to integrating normally incompatible cryo-EM and cryo-ET software into pipelines. With useful features scattered among different programs (e. g: AreTomo's unsupervised alignment  [2] , Topaz's denoising  [8] , CrYOLO's filament picking  [4] , EMAN2's trainable segmentation  [10] ), and numerous small custom scripts developed by researchers tailored to a specific project's needs (distributing or selecting particles, improving alignments, etc), software integration is a real and common concern. Even when a compatible tool is available or conversion possible, it is often hard to tell when it worked properly due to lack of generalised visualisation tools for inspecting and validating data throughout the pipeline. Due to the aforementioned compatibility restrictions, popular software suites (such as IMOD and Dynamo) often provide built-in visualisation tools, duplicating development efforts and further deepening the separation between pipelines.
Finally, many existing visualisation tools are not easily hackable by users to extend them with custom functionality. Even those that offer ways to extend their functionality (such as ChimeraX  [19]  through its Python API or Dynamo with MATLAB [20] code), provide limited interface to data and rendering code, or require considerable programming skills to do so. To address these issues, we present blik, a new software for interactive visualisation, manipulation and analysis of cryo-ET data. The code is open-source and open to community contributions at https://github. com/brisvag/blik). blik is a plugin for napari  [21, 22]  (https://napari. org/), a visualisation software focused on scientific imaging, with data segmentation and annotation available as core features. It has both a programmatic and a graphical interface, allowing for seamless integration of interactive visualisation and scripted pipelines.
napari offers great customization options, powerful built-in tools and a growing plugin ecosystem, which allows blik to focus on specific cryo-ET needs. To address the challenges listed above, blik's design choices, features, and architecture reflect the following primary goals: • Compatibility: blik can read and write data in file formats from a variety of different software suites, including IMOD, Relion, and Dynamo. This makes it easy to switch between tools or to integrate custom scripts into a workflow. Moreover, the scientific Python ecosystem is well-established in many imaging fields, and is starting to be a player in the cryo-EM and cryo-ET world. By adhering to the practices and conventions of this ecosystem, blik allows to easily integrate many existing field-agnostic tools (e. g: scikit-image and scipy for image and annotation processing, pytorch and the plethora of Python machine-learning November 23, 2023 2/16 (ML) tools for several types of analysis) and take advantage of existing solutions and avoid the tendency of "reinventing the wheel" that scientific software can often be prone to.
• Interactivity: blik provides interactive visualisation that allows users to explore data programmatically and visually at the same time. Data is always accessible through a standard Python console and in simple, well established formats such as numpy arrays and pandas dataframe. This makes it easy to validate data during processing and to identify problems as soon as they arise, as well as to provide a framework for quicker prototyping and debugging of new workflows. • Hackability: blik is open-source and easy to extend with custom functionality. This allows users to tailor the software to their specific needs. The napari plugin ecosystem also allows taking advantage of many existing analysis tools, even from different imaging fields. Additionally, blik's Input/Output (IO) capabilities are easily extensible by users to include new custom formats.
• User-friendliness: blik's, and most of napari's, functionality are also exposed in the Graphical User Interface (GUI), making it also easy to use for non-programmers. • Performance: thanks to napari's visualisation backend vispy, blik has performant rendering which can handle large 3D (and more) datasets, even larger-than-memory thanks to dask. • Community: to foster community contribution, code reuse, and jumpstart other projects, many contributions were upstreamed to napari, vispy, or extracted into simple single-use libraries usable by other projects. Finally, to better allow users to seamlessly integrate blik into their existing workflow, blik is currently being integrated into Scipion as a plugin (https://github. com/scipion-em/scipion-em-blik).
Results Visualisation blik relies on napari for performant visualisation. The napari core is field-agnostic, and requires the development of custom readers and writers to convert specific file formats into a napari visualisation. This has been an important focus of blik so far, through the development of cryohub and the napari representation of particle poses as oriented points. Data in napari is exposed as layers that can be controlled individually (similarly to general image processing software like GIMP or Adobe Photoshop). There are several types of layers; the main ones used by blik for visualisation purposes are: Image for images and volumes, and Points and Vectors for particle poses.
Firstly, the image texture is sampled in a NxM grid (for a kernel of shape NxM) centered around the texture coordinates. A weighted average is then computed based on the kernel weights and the resulting value is forwarded to the rest of the shader. • A power spectrum widget, which quickly computes the power spectra of single images, stacks or volumes. Power spectra are a fundamental tool for data inspection and validation in cryo-EM, from estimating resolution, to generating hypotheses about symmetry, to finding caveats in the data collection procedure. Like any other computation made by blik or napari, the power spectrum is then available as a normal numpy array for further use from within Python or to export to the available formats. Additionally, masks and pixel-based segmentations (often called just "segmentations", or "labels" in napari jargon) are easily displayed (and modified with all the tools provided by napari, such as free-hand painting, even in 3D) by using the napari Labels layer, which is designed specifically to work with segmentation data (binary or integer arrays) (Fig 1F the napari ecosystem, albeit mostly in the field of fluorescence and optical microscopy.