Bibliographic record
Abstract
Abstract Previously, in Chapters 1–3, we saw how AI is a new form of agency that can deal with tasks and problems successfully, in view of a goal, without any need for intelligence. Every success of any AI application does not move the bar of what it means to be an intelligent agent. Instead, it bypasses the bar altogether. The success of such artificial agency is increasingly facilitated by the enveloping (that is, reshaping into AI-friendly contexts) of the environments in which AI operates. The decoupling of agency and intelligence and the enveloping of the world generate significant ethical challenges, especially in relation to autonomy, bias, explainability, fairness, privacy, responsibility, transparency, and trust (yes, mere alphabetic order). For this reason, many organizations launched a wide range of initiatives to establish ethical principles for the adoption of socially beneficial AI after the Asilomar AI Principles and the Montreal Declaration for a Responsible Development of Artificial Intelligence were published in 2017. This soon became a cottage industry. Unfortunately, the sheer volume of proposed principles threatens to overwhelm and confuse.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".