Unraveling the complexities of learning in community development evaluation
Bibliographic record
Abstract
In this article, we critically explore the pedagogical implications of evaluation in the community development sector, with a focus on opportunities for enhancing learning through evaluation in community contexts. We begin by situating learning as foundational to evaluation, whether individually or in collaboration with others, and through reflection, dialogue, critical thinking, or practice. We then shift to a discussion of community development and related principles of practice, followed by a focus on evaluation and associated challenges in community contexts. Throughout, we interweave concerns with neoliberalism as a key aspect of the evaluative state, documenting effects on the pedagogical potential of evaluation. In the final part of the article, the authors shift to the possibility of providing critique as a way to resist neoliberal discourses and empower communities to develop what they believe are necessary and more realistic alternative discourses of evaluation.
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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 | 0.263 | 0.290 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.013 | 0.106 |
| Scholarly communication | 0.041 | 0.054 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".