Bibliographic record
Abstract
Who is the enemy? In May last year, I knew exactly who the enemy was. It was someone who sought to entice me away from completing a manuscript on which I was working relentlessly. I was on long leave, a Ford Foundation Scholar in Residence, trying to complete a book about the connections between my life experiences, on the one hand, and my decision-making processes as a judge, on the other. The more attractive the seduction, the greater the danger. I succumbed, but not completely. The invitation was to participate in a research workshop in Toronto on Critical Issues in International Refugee Law. And since I was actually going to be in Toronto at the time, I gave way a little … could I prepare a few notes at the last minute, and then make an impromptu presentation? The organisers agreed. The theme I promised came to me from the book I was writing – The Strange Alchemy of Life and Law . Hence the title of the proposed talk: From Refugee to Judge of Refugee Law . I made a point of getting to the workshop an hour before my presentation was due. The plan was to give myself enough time to pull a few thoughts together. But the paper I heard on my arrival was so interesting that I decided to postpone any note jotting till discussion-time.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".