Bibliographic record
Abstract
We begin by expressing our appreciation for the opportunity to take part in what we regard as an important dialogue concerning some of the responses that have arisen out of the commentaries on Spielraum and Teaching. For the most part, it seems evident that our paper and the accompanying commentaries by David Solway and Clive Beck and Clare Kosnik speak from very different approaches to understanding teaching. As such, we are quite willing to allow readers to make up their own mind as to their value. In other words, we see little advance in a Reproof Valiant that ends up playing citation against citation where we tender one scholar (i.e., Bourdieu, 1990) against another (i.e., Sch6n, 1983). Rather, our purpose here is to clarify our position and comment on related issues. Our approach is threefold. First, we note some of the initial concerns that convinced us to seek an understanding of practice other than the one proposed by Schon. Second, we articulate an approach to learning to teach that follows from this understanding. Finally, we move to what we see as one of the more interesting issues that arise from the all three papers, namely, the limitations of learning to teach through reflection on practice.
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 | 0.009 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.023 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".