Bibliographic record
Abstract
This paper puts forward a pedagogical model of care for K-12 educators that is specifically focused on alternative classroom educators. In conversation with educational theorists and psychologists, a model of care that is translatable to both teachers and students in non-traditional classrooms is presented. Looking first at Arlie Hochschild’s “emotion work” in the context of alternative classroom teaching, a link is made to Nel Noddings’s “ethics of care” as a pedagogical starting point. The author then riffs on psychoanalyst D.W. Winnicott’s notion of the “good enough mother,” the one who “manages a difficult task: initiating the infant into a world in which he or she will feel both cared for and ready to deal with life’s endless frustrations” (Alpert). Connecting Alpert’s mobilizing of Winnicott to aspects of Noddings’s “caring relation” builds a theoretical bridge that supports and scaffolds the construction of what the author calls the “good enough teacher.” The author also suggests that this pedagogical model of care might also be replicable by students who need to take care of themselves. Throughout the paper examples are drawn from the author’s experiences as a teacher and learner in a variety of alternative education classrooms.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".