Bibliographic record
Abstract
When I set out to write a short speech on Frank's career at the law school, I thought it would be a good idea to talk to as many people as I could who had been students or colleagues of Frank's.I was overcome by a veritable tidal wave of tributes, praises, and outpourings of sentiment bordering at times on the downright maudlin.I got edgy and defensive.I urged people to come clean.There must be something a bit off-colour, perhaps a trifle embarrassing, that you could tell me about Frank.Just a little bit.Please.Nothing.Well, almost nothing.After beating people about the head for their unfailingly effusive praise I managed to drag something out of one of my former classmates.What did he say?Well, that would be telling.I'm going to sit (or stand, as the case may be) on that for a while.This is a day in honour of Italian Canadians (and of course Frank in particular).Of course, this is why I was chosen to speak about Frank.It is not well known but my ancestors are not really from Scotland.My father changed his name from MacInCini to MacIntosh many years ago.Something about persistent creditors.There is much too much to say about Frank in a mere twenty minutes.But let me start off by noting that I have some small sense of Frank's
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".