Bibliographic record
Abstract
My brief for this piece was to write on human rights.That left two main options.I could undertake a fairly specific black letter critique of bills of rights.I am a strong opponent of these instruments, in either their entrenched, constitutionalised form or in their statutory, enacted form.The former you see in Canada and the United States of America; the latter you see in New Zealand, the United Kingdom and in Victoria.In my view both forms are pernicious; both forms undermine democratic decision-making; both forms unduly enhance the point-of-application power of unelected judges on a host of issues that are in effect moral and political ones -ones over which judges (committees of ex-lawyers as Jeremy Waldron continually reminds us) have no greater expertise, no superior moral perspicacity, no better pipeline to God than the rest of us non-judges, otherwise known as voters.Here I have chosen the other option, writing about human rights more generally -what they are; where they come from; what people presumably mean when they invoke this abstraction of 'human rights' and when they intone, rhetorically, 'Don't you want your rights protected?'
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.040 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 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".