Bibliographic record
Abstract
This collection of articles and practice notes reflects current trends and important themes in our field.As evaluators, we are increasingly required to conduct evalu ations of complex interventions that have moved beyond simple program struc tures.We must innovate and consider the cultural, environmental, and political contexts within which we work in order to serve our clients and stakeholders to the best of our abilities.The articles included in this issue provide evidence to support some of these innovations, whether they involve evaluating new ways of working together (McKellar and her colleagues), working with Indigenous com munities (Gillespie), structuring our evaluation efforts against rigorous theories of change (Lam), or considering program sustainability as part of our evaluations (Mayne).The practice notes thoughtfully submitted for this issue are also focused on innovation in evaluation: Dinca-Panaitescu attends to the evaluation of Social In novation Labs, which represent an emerging intervention in many fi elds; Lavelle focuses on the professional practice of evaluation and how to teach communica tion skills; Renger and his team describe an example of System Evaluation Th eory; and Halar et al. share their experience in developing a collaborative evaluation framework.All of these papers include concrete illustrations from the fi eld and inspire us to continue to innovate and improve our evaluation approaches, meth ods, and interpersonal skills.Finally, Gowensmith and O'Reilly contribute to our continued professional learning through two book reviews-I encourage you to read them and fi nd in spiration for your own practice.Thank you to all of our authors for these excellent contributions to evalua tion research and practice.In closing, I would also like to encourage our readers to send me their thoughts and comments on CJPE papers.I recently received one such letter from Dr. Oralia Gomez-Ramirez, an emerging evaluator keenly interested in the paper recently published (in CJPE 35.1) by Lawson, Hunter, and McDavid on the current profile of the Credentialed Evaluator designation.Dr. Gomez-Ramirez provided insightful comments on the credentialing program and shared with us some interesting suggestions for the integration of emerg ing evaluators into this process, which have been passed on to CES.This type of conversation is exactly what we are trying to achieve; I am pleased to see that our articles, practice notes, and peer reviews are stimulating thought, discussion, and new ideas.
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 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.145 | 0.104 |
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".