Foundations for a Utopia: Can Evaluation Contribute to Achieving a Better World, and How?
Bibliographic record
Abstract
“Considering that we have 10 years to radically transform our societies to make them sustainable and equitable, what would be the most important action that we should implement in our practice, as evaluators?” Nine experienced evaluators, representing a variety of practices, cultures, contexts, and genders, agreed to address this question. With this article, we explore how the evaluation field could help implement the foundations for achieving this utopia. After receiving the testimonies, a content analysis was conducted to identify the salient themes. The objective was not to find a consensus based on the suggestions but to create meaning from the different ideas. The draft analysis was shared with the participants, who were invited to become co-authors. Some voiced their skepticism on the capacity of the field to envision a new utopia. All indicated the need for evaluators to transcend the status quo mindset, emphasizing the importance of working with communities to increase their self-determination. One participant also raised the importance of contributing to larger social movements. The implications for the role of the evaluator are discussed.
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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 | 0.326 | 0.291 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.015 | 0.064 |
| Scholarly communication | 0.030 | 0.033 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".