Bibliographic record
Abstract
In May 2003, academics gathered at the Dalhousie University in Halifax, Nova Scotia, for the annual meeting of the Congress of Humanities and Social Sciences/Canadian Federation for the Humanities and Social Sciences, with various educational societies meeting over a period of days.An all-day symposium was organized by the Centre for Policy Studies in Higher Education and Training, University of British Columbia.With the support of the congress and the federation, we were able to draw national attention to our symposium.The day also had the co-sponsorship of the Canadian Society for the Study of Education (CSSE), the Canadian Association for the Study of Adult Education (CASAE), and the Canadian Society for the Study of Higher Education (CSSHE).The session we organized began with a keynote address delivered by Dr. Sheila Slaughter, University of Arizona, co-author with Gary Rhoades of Academic Capitalism and the New Economy: Markets, State, and Higher Education (2004).The address was followed by a series of paper presentations from scholars across Canada, the United Kingdom, and Argentina.Approximately fifty people attended the whole day, with interested scholars attending during various portions of the day.Attendees at the colloquium overwhelmingly indicated the importance and timeliness of the dialogue that was generated through the paper presentations.Animated discussion occurred throughout the day.Some of the discussion focused on questions such as the following:
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.501 | 0.302 |
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".