Bibliographic record
Abstract
As I was preparing this article1 about the Council of the Federation, about the manner in which it differed from its predecessor, the Annual Premiers’ Conference, my thoughts were constantly harking back to my favorite English author, Jane Austen. Although the titles of her novels are different, and despite the fact that Elizabeth Bennet is not an exact replica of Elinor Dashwood,2 Jane Austen always writes the same story: the battle between reason and emotion, between sense and sensibility. Now, quite frankly, as do Alain Noël and others before me, I believe that the Council of the Federation is not more than a light institutionalization of the Annual Premiers’ Conference.3 It is the same story again. And one that also has to do with the tension between sense and sensibility. During my preparation, I also recalled the very first sentence of Jane Austen’s masterpiece Pride and Prejudice which runs as follows: “It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife.” Amusingly, the Council of the Federation’s philosophy could be articulated in a similar fashion: “It is a truth universally acknowledged, that a federal government in possession of a good fortune, must be in want of provinces.”
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.008 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.014 | 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".