WITTGENSTEIN’S CONTEXTUALIST APPROACH TO JUDGING “SOUND” TEACHING: ESCAPING ENTHRALLMENT IN CRITERIA‐BASED ASSESSMENTS
Bibliographic record
Abstract
Abstract Comparing the early, analytic attempt to define “sound” teaching with the current use of criteria‐based rating schemes, Jeff Stickney turns to Wittgenstein’s holistic, contextualist approach to judging teaching against its complex “background” within our form of life. To exemplify this approach, Stickney presents cases of classroom practice (reexplanation), auditioning dance students, teacher inspection, and mentoring student teachers. These examples highlight problems with the epistemological and criterial construal of teaching, in that both sets of rules tend to constrict unnecessarily the ranges of “reasonable” practice. Shifting to the contextualist approach, according to Stickney, reveals these occluded, political aspects of assessing the “soundness” of teaching and invites a renegotiation of arbitrary limits.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.004 | 0.072 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".