Bibliographic record
Abstract
David Schaefer was unable to co-present this paper at the conference. As such, I was sole presenter of this work. It was well received by about 20 delegates who all received hard copies of the powerpoint presentation. Discussion was rich (about half an hour) after the presentation where delegates had questions in regards to the methodology of the research, antidepressant medication being prescribed by a host of other illnesses and diseases that aren’t related to depression, and whether or not psychopharmacology training should be mandatory, or not, in counsellor education. David Schaefer and I have discussed what delegates talked about and are continuing to proceed with a manuscript preparation of this research to the journal ‘Canadian Psychology’. Furthermore, we have a manuscript currently in review with the Canadian Journal of Counselling, which focuses specifically on the pedagogical implications of the research. Overall, the presentation was a success and I much appreciate the support of the AU APDF to attend the Canadian Counselling Association Annual Conference.
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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".