Bibliographic record
Abstract
Paul Dell, a family systems therapist inspired by the systems thinking of Humberto Maturana, posits that family systems achieve pathology because of what he calls “epistemological errors”: either the refusal to acknowledge reality or the desire to control reality. Reality, in Dell’s definition, is the coupled nature of human interaction, or structure determinism. Applying Dell’s definition to classrooms, I identify two epistemological errors commonly committed by teachers: valuing content more highly than relationships in the classroom and attempting to control students through classroom management techniques. When these two practices are viewed through the systems lens rather than through the modernist, objectivist lens, the relationships that are enacted in a classroom among teacher, students, and the content under study come into focus, and pathology, or repetitive behaviors that obviate desired learning, is more easily discerned. Given the emphasis systems theory places on relationships, I claim that, as with family systems, classroom systems can benefit from the kind of analysis—or “therapy”—that exposes the “coherence,” or the tight relational couplings, within the system that, in some cases, invites non-educative interactions. Such therapy can help teachers shift their own attitudes and behaviors so as to influence those of their students.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.053 | 0.027 |
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".