Praxis Makes Perfect? Transcending Textbooks to Learning Evaluation Experientially and in Cultural Contexts
Bibliographic record
Abstract
Abstract: The theory-to-practice loop is riddled with gaps, incongruencies, and, at times, trauma when it comes to the professional development and practice of evaluators. Our current system of professional development for evaluators systemically and institutionally reinforces racism, white privilege, and misogyny, thus re-creating harm and the barriers that so many BIPOC and LGBTQ2S evaluators are working hard to overcome. This article provides the reader with an alternative to the field’s valuing and learning evaluation within “institutions of higher education” and other “formal” and “scholarly” learning spaces. Rather, it provides for a balanced approach of experiential learning in the field and within cultural contexts as a much-needed professional design component for developing responsive, effective, and transformative evaluators. Praxis and experience should have at least equal value, merit, and worth for developing current and upcoming evaluators. When done correctly, wisdom to evaluative thinking, development, and practice happens, and not simply reinforcing and generating the same evaluative voices, constructs, and behaviours of the privileged evaluation patriarchy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".