Commentary: Who measures those who measure? The future of assessing research activity in Canadian psychology.
Bibliographic record
Abstract
The recent article in Canadian Psychology by Carleton, Peluso, and Asmundson (2010) assessing the research activity of Canadian psychology departments with graduate programmes has generated substantial feedback. We are very pleased to have provided not only an initial data set for review by departments and individuals, but also to have prompted what we believe is an important discussion. The current article responds to a thoughtful critique and extension of our initial work (Symons, 2011). Herein we address and expand on each of the comments raised by Symons in an attempt to fully delineate the successes and challenges for our profession as we take the next steps in this area of research. Also, in light of the review and other anecdotal feedback received to date, we make further recommendations for using the data available from the Web of Knowledge, the rankings we provided, and the data provided by Symons. We conclude with directions for future research that we believe will help further the area.
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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 | 0.054 | 0.326 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.012 | 0.004 |
| Research integrity | 0.038 | 0.038 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".