From HAL to GenAI: Optimizing chatbot impacts with CARE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article explores the evolution and prospects of conversational chatbots, specifically the latest generation referred to as Generative Artificial Intelligence (GenAI) chatbots. This article comprehensively examines GenAI chatbots’ business applications and impact across macro, meso, and micro levels of organizations. At the Macro level, this article explores how GenAI chatbots reshapes industry dynamics. The Meso perspective delves into organizational changes, while the Micro lens focuses on enhancing individual productivity, learning, and creativity. However, GenAI chatbots’ immense potential is accompanied by risks in four META areas – Matching, Ethics, Technology, and Adaptability. In response to these challenges, the article introduces a human-centric CARE framework – Collaboration, Accountability, Responsiveness, and Empowerment – to mitigate the risks and optimize the impacts brought by GenAI chatbots. This work provides practical guidelines to navigate the complex landscape of GenAI implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it