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
<p>AI is everywhere around us and affecting our lives in many ways, some of which we may not even be aware of. This paper discusses the advantages and disadvantages of AI and its impact upon society. The authors, Peter, and Laura are both disabled which means that, inevitably, they view AI and the ethical issues which it brings, through the lens of disabled people. However, they try as much as they can to discuss the ethical issues around AI in an unbiased manner. This commentary starts by introducing the concept of AI, the authors and their disabilities, the meanings of ethics and goes on to discuss the ethical issues which Peter and Laura see in their day-to-day lives. They then drawfromthe literature on AI and ethics to broaden the discussion to take account of current published work by others on the topic. Finally, they return to their own perspectives and conclude that AI offers many advantages to society and our future. However, they also warn of the dangers and ethical challenges which AI raises. It is hoped that this commentary frames a contribution to the field of AI and ethics which readers will find interesting, useful and, perhaps, challenging.</p> <p>&nbsp;</p>
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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