Bibliographic record
Abstract
This paper makes the case that Stanley Cavell’s thinking on conversion, eveloped in “Normal and Natural” in The Claim of Reason, offers resources that can be used to develop a politics that acknowledges the importance of learning from the voice of skepticism instead of seeking to silence the skeptic through the pursuit of policies and practices that promise a type of certainty that will forever silence skepticism. I develop this case from my position as a teacher educator who knows very well the desire to silence skepticism in the form of finding a way of teaching future teachers so that I/we can be certain that they will be effective and engaging educators after graduation. Giving up the belief that we can achieve certainty when it comes to teacher preparation does not consign us to hopelessness, but it does suggest that teacher educators may have more to learn from listening to the voice of skepticism than is suggested by current discourses in teacher education. Though I write from the position of a teacher educator and my examples are drawn from the work of teacher education, the main goal of this paper is to develop a reading of “Normal and Natural” that may help us appreciate new dimensions of the political implications of Cavell’s work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.041 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".