Twenty Years of Student Scholarship: Celebrating the Dalhousie Journal of Legal Studies
Bibliographic record
Abstract
In the case of the Dalhousie Journal of Legal Studies that person was its first editor-in-chief, Ryerson Symons. In the production of the first volume, released in 1992, he was joined by an 11-person board of directors, a 30-person editorial board, a 10-person notes and comments editorial committee, a 12-person book reviews editorial committee, a large number of managers and assistants, five founding patrons, and a six-member advisory board. A lot of people were persuaded that the Journal had merit. I might continue on the theme of identifying the characteristics of successful projects. If a good project has an early champion and a legion of people who see its merits, it has to be able to outlive those early visionaries. A friend once pointed out, a project is lousy if it fails to survive you. The Journal has not just survived, it has thrived. Twenty volumes. A remarkable accomplishment that does great honour to its early advocates.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.015 |
| Scholarly communication | 0.029 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 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".