Bibliographic record
Abstract
Brad Cousins] Joe, I am truly delighted with the opportunity to talk with you.I know that our readers are interested in learning about how the Journal got its start 20-plus years ago, but fi rst, could you describe your career trajectory and the development of your interests in evaluation?[Joe Hudson] Sure … I grew up in New Westminster, spent some time in the British Merchant Marine, fi nally graduating from UBC, and then went to Prince Albert to work as a Classifi cation Offi cer in the Saskatchewan federal prison.That experience drove my interest in group work and, more generally, social work, so I enrolled at the University of Minnesota, graduated with a Master's degree in Social Work, and returned to the prison with great expectations of bringing about the kind of changes I thought were so badly needed.Those expectations quickly evaporated.The warden was not terribly impressed with my new degree and had quite different ideas about how to run a prison.Since he had the power to carry through with his views, and I didn't, I fi nally resigned, going to Calgary to work
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 | 0.010 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".