Bibliographic record
Abstract
I am pleased to introduce this new issue of the Canadian Journal of Program Evalu ation, which contributes in an important way to the French-language literature base in evaluation through three distinct articles.First, the paper by Souratié, Sos sa, and Paul provides a descriptive analysis of evaluative practice in the health sec tor in Burkina Faso, through a review of 10 evaluation reports published in the last 10 years.This analysis supports further evaluation capacity building and provides ideas about to how this can be accomplished.Turcotte, Rey, and Brousselle position developmental evaluation as a potential solution to the challenges encountered in evaluating social innovation projects and programs.This interesting paper enables a dialogue between the literature in this field and experienced practitioners, and it provides concrete advice for those interested in using developmental evaluation approaches.Andrée-Anne Houle and her colleagues contribute a methodological piece on the use of journals or logbooks in evaluating programs.They provide an example of how journals were used in the implementation of an anxiety-reduction program for teens.Finally, to round out our paper offerings, Iyamu, Berger, Ono, and Salmon describe practical steps in defi ning effectiveness principles within the broader context of principles-focused evaluation.Three examples are provided to highlight lessons learned as part of this process.This issue also includes two Research and Practice Notes.Th e first, written by El Hassar, describes an evaluation capacity needs assessment tool and framework, developed to support organizations in determining how best to undertake evalua tion capacity-building activities.The second, penned by Miciak, Barrington, and Lavoie, features the competency of reflective practice as it is described in the CES evaluator competency framework and describes a six-step reflective process that can be undertaken through voice memoing.Finally, our issue closes with Brenda Cherkas's book review of Russ-Eft and Sleezer's Case Studies in Needs Assessment.I hope that all CJPE readers will find some interesting and thought-provoking articles in this diverse issue that spans geographical, epistemological, and practical boundaries.As always, I extend my gratitude to the CJPE editorial team and all of the reviewers who have made this issue possible.
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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 | 0.014 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.018 | 0.022 |
| Insufficient payload (model declined to judge) | 0.061 | 0.039 |
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".