Some Thoughts about the Evolution of Evaluators ’ Methodological Identity Presented at the Canadian Evaluation Society
Bibliographic record
Abstract
Thank you for the generous invitation to join the CES gathering this year. I am very honored to be a part of your 25th anniversary celebrations. I hope my contribution will be generative and also respectful of Canadian traditions and histories. My comments today offer my perspectives and my reflective thoughts on the character and history of evaluators ’ methodological identity. The comments briefly trace the evolution of our contemporary methodological history (meaning the latter half of the twentieth century), not so much in terms of specific methodological advancements – although there have been many of these – but rather in terms of the role methodology has played in our work and in our understanding of ourselves as evaluators. We have long been a methods-driven field. But, what did this mean in the past, what does it mean now, and are we still a methods-driven field? In what ways is the understanding of the practice of evaluation still mostly about methods – both inside the evaluation community and without? And how does our methodological identity matter – for the quality, credibility, and practical import of our work? These are the questions I will address and, in doing so, will paint a portrait of evaluation methodology that is today far more multi-layered and
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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 | 0.166 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.061 | 0.054 |
| Scholarly communication | 0.036 | 0.012 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".