Bibliographic record
Abstract
I IntroductIonI made this remark to a colleague from the Canadian Department of National Defence in the margins of a seminar in 2015 in Montreal.It was a facetious remark intended to affirm my colleague and his excellent points, through a satirical device.I had not, at the time, fully explored the meaning behind the remark, but in retrospect I find that there is a lot of meaning and that it is a useful tool to organise my arguments in this paper.First, the remark is true because too much is sometimes expected of the law and legal advisers.Secondly, the reference to 'high priest' suggests a formal, stratified regime of pontification on the law and its related elements.Thirdly, I do belong to a secular military.Fourthly, it does raise a question: if the lawyer is not responsible for moral elements of operational decisions, who is? Fifthly, I suggest that we do not need to allocate responsibility to a particular person for the moral element.Sixthly, the remark is facetious because the concern does not actually warrant formal escalation -and, of course, I'll explain why I take that position.Finally, I'll suggest an antidote -notwithstanding that it involves a difficult 'pill to swallow'.28 Department of Defence, 'The Defence Values' (n 25).29 Jared M Diamond, Guns, Germs and Steel: The Fates of Human Societies (WW Norton & Co, 1997) 268.30
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".