Bibliographic record
Abstract
CJPE 31(1) is jam-packed with practical learning interspersed with research on evaluation that both enhances our academic understanding of evaluation and advances the practice of evaluation.Carreau et al. lead the way with a study that fi ts squarely into the category of empirical research on evaluation.Th is team not only conducted research on evaluation, but also used results to identify weaknesses in current evaluation methods in the fi eld of interprofessional education and collaborative practice.It is a strong example of well-done research on evaluation that has direct practical applications.Th e next three full-length articles depict innovations in evaluation approaches and methods.Evaluability assessment is all too forgotten and is rarely discussed.Soura et al. 's French-language manuscript brings evaluability assessment to life.Readers should note the utility of well-done evaluability assessment and will perhaps be inspired to include it in their evaluation toolkit.Michelle Searle and Lyn Shulha open our eyes to arts-informed inquiry as a methodological tool for evaluation.I recall being mesmerized by Michelle's CES conference presentation on this approach and am delighted to see it published here so that others can learn, adopt, and adapt.Th e fi nal research article, by Arsenault et al., takes us into the deep, dark, and, yes, scary environment of prisons to demonstrate how we can adapt our approaches to unusual and challenging contexts.Th e four short articles in the Research and Practice Notes section demonstrate how varied the practice of evaluation is.I believe that Williamson et al. 's piece is a CJPE and CES "fi rst": a piece published by student participants on how the Student Case Competition contributed to the development of specifi c evaluation competencies.Nutter et al. draw our attention to challenges and solutions to conducting a "needs assessment"-something evaluators are oft en called upon to do and that some might argue is not a typical evaluation pursuit.Henson argues that standardized evaluation questions can be modifi ed to assess the quality of data generated by programs for evaluation-a case of evaluators helping others to help evaluators.And Renger returns to these pages with some colleagues to share how to conduct process fl ow mapping as part of continuous quality improvement.Jam-packed, fun-fi lled, and, I think, with at least one thing for every reader!
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| grok | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| opus | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.018 | 0.018 |
| Insufficient payload (model declined to judge) | 0.115 | 0.080 |
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, unvalidatedLabeled directly by 3 models reading the full record.
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".