Bibliographic record
Abstract
Abstract The chapters in this volume set a rich context for understanding the challenges that environmental evaluators face in their everyday work. In particular, the authors highlight the need for responsive, contextual, flexible, adaptive, multidisciplinary, and mixed‐methods evaluation approaches. In this chapter, I reinforce their call and further suggest that they (1) increase their attention to developing logic models based on their theories of change, (2) continue exploring the use of complexity theory to ground their evaluations, and (3) consider reframing some evaluations to being strength‐ and asset‐based, where vibrancy, resiliency, and rebirth can be studied. Finally, in response to the authors' concern for their own development, I comment on the possibilities for evaluation capacity building and the formation of networks and communities of practice. © Wiley Periodicals, Inc.
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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 | 0.595 | 0.662 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.061 |
| Scholarly communication | 0.040 | 0.037 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.021 | 0.039 |
| 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".