Bibliographic record
Abstract
I am sometimes asked why I chose to write a book about the RDS case. 1 The idea stemmed from a first-year criminal law class that I was team-teaching at the University of Ottawa Faculty of Law in 2008.As many law professors will agree, we never have enough time to teach cases properly, and in this course, RDS was bundled together with a few other cases in one lecture.My colleague Professor Rosemary Cairns Way and I then asked the students what they understood was the principle that came out of the Supreme Court ruling.With great confidence, they responded, "It's a good decision.All judges should be impartial."And I was repeatedly dumbfounded when the same exchange occurred year after year, because that is so not the message that I took out of RDS.The actual message, at least to me, was the peril of assuming that judging is-or can be-impartial.To try to unpack the seeming simplicity and beguiling obfuscation of the concept of "impartial judging," I thought, would take more.It could take up an entire course.
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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.035 | 0.033 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.023 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".