Bibliographic record
Abstract
Abstract This chapter turns from judges to human rights lawyers, whose role as advocates gives rise either to different problems or to the same ones in more overt form. It focuses on writings intended for the general public by Shami Chakrabarti, Conor Gearty, and Anthony Lester. All three are publicly prominent British lawyers, whose views echo and amplify those reported in previous chapters from judges in Strasbourg and Ottawa, and from Human Rights Watch in New York. The chapter argues that their advocacy for the rights of individuals is vitiated by habitual cynicism toward government, and a constantly deaf ear to its genuine concerns. Since it cannot persuade sceptics, this is poor advocacy. Moreover, since it is widely acknowledged in principle that few rights are absolute and unconditional, it follows that there are circumstances when it would be proportionate for rights to be limited or suspended, or not to be extended. Therefore, human rights lawyers should be more willing than are these three to think about what those circumstances would be, and to recognise them when they obtain, instead of treating every concession to circumstance as if it were a grubby betrayal of principle. A defence of rights that fully accepted that imperfect compromise can really be inevitable, and that acknowledged that sometimes the claims of the social good really do justify exposing individuals to greater risk, would be a more honest defence, and much the stronger for it.
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 | 0.025 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.030 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.020 | 0.026 |
| Insufficient payload (model declined to judge) | 0.007 | 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".