Bibliographic record
Abstract
As if relying on a camera with a telephoto lens to capture a photo of a caring school, Kevin Currie-Knight has presented a picture of markets as most able to offer attentive and responsive schools.I suggest that he has the wrong lens on his camera and is taking the wrong picture.Getting a close view of caring relations within schools would require a macro lens that enables close-up picture taking.The caring relation in the frame with a macro lens is between teacher and student.Another snapshot may be of student and student, teacher and parent, or administration and teacher.But this lens doesn't allow for a picture of student and school or parent and board, for it isn't schools that care, but people within those schools.Schools, private or public, should be examined for how best they create conditions for care, not for how best they care.We are in for disappointment if we are trying to create caring systems rather than systems within which care may thrive.This is a subtle shift of focus, but one that I believe makes all the difference.First, however, allow me to give an overview of Currie-Knight's argument and my responses, and then come back to taking pictures of care in the classroom.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".