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 considers whether a decision made by generative artificial intelligence can satisfy the standard of reasonableness set out in Canada (Minister of Citizenship and Immigration) v. Vavilov. Vavilov requires that administrative decisions be justified through reasons that are transparent and intelligible to the affected party. Earlier scholarship, law, and policy have assumed that AI cannot do this because it cannot provide reasons and its inner workings are opaque or uninterpretable. However, new capabilities of large language models challenge this view. Recent experiments show that when prompted with party submissions and relevant legal materials, generative AI can produce persuasive, legally grounded reasons for decisions. The article evaluates two responses: one argues that AI decisions remain unreasonable under Vavilov since their true basis lies in opaque technical processes; the other contends that Vavilov focuses on the cogency of stated reasons, not how they were generated. The article supports the latter position, suggesting that Vavilov leaves open the possibility that AI-generated decisions can be reasonable, provided their reasons meet the decision-making standard applied to human actors.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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