Steps Toward Evaluation as Decluttering: Learnings from Hawaiian Epistemology
Bibliographic record
Abstract
This paper discusses one of the more contemporary challenges in development and in global health--lots of good ideas from well-meaning insiders and outsiders that end up cluttering both the physical and mental spaces of what can be loosely termed as “attempts” at development. Given the place-based nature of indigenous thought, we turn to Hawaiian epistemology at looking to insights for clarity on how one can negotiate interactions to declutter place and also confuse identity. We believe that evaluation as a field can help in bringing greater recognition of the need for models of development and learning that respect the importance of de-cluttering. Implications for a decolonized approach to evaluation 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 | 0.099 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.086 |
| Scholarly communication | 0.021 | 0.027 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.004 | 0.010 |
| 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".