Implementing Culturally Responsive Evaluation Methods: Reflections on Challenges to Traditional Understandings of Power, Validity, and Rigor
Bibliographic record
Abstract
In this reflective article, the evaluation team for the NASA-funded SciAct STEM Learning Ecosystems project discusses the process we used to study four exemplar STEM learning ecosystems in regions across the US using culturally responsive evaluation (CRE) methods. We expected to follow a linear path for each of the study’s three inquiry cycles but found that this process needed to be fluid because of the culturally responsive evaluation methods employed. These methods meant we shared control with the project team in ways we had not before and had to rethink study validity and rigor. Beyond the evaluation team, this article shares reflections from the project team as well as the study’s external evaluation advisor about what it was like to participate in a study that used CRE methods.
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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 | 0.711 | 0.728 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.068 |
| Scholarly communication | 0.030 | 0.032 |
| Open science | 0.010 | 0.019 |
| Research integrity | 0.014 | 0.041 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".