Bibliographic record
Abstract
Attention is drawn to the movements of the body and to the ethical imperative that emerges in compelling, flowing moments of teaching. Such moments of teaching are not primarily intellectual, discursive events, but physical, sensual experiences in which the body surrenders to its own movements. Teaching is recognized momentarily as a carnal intensity embedded in and emerging from the flesh. The ethical imperative to this teaching is felt proprioceptively and kinaesthetically when one holds in self-motion the well-being of another as being of the same flesh. The teaching caress offers a primary example. This gesture of intimacy discloses an embodied ethic that contrasts with the transcendental ethics of curricular prescriptions, professional codes of conduct, and the presumptions of self-monitoring behavior. It is a gesture of care for another person, without fastidious carefulness. It is a gesture of pure duration, without sanctimonious purity, in its contact with the beauty, truth and value of the teachable moment. From earliest engagements with children to the dynamics of the university classroom, what makes for good teaching is essentially attentiveness to intimate gestures, such as the caress, that guide teachers kinethically in the moment.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.056 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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 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".