Respect by Design: How Different Educational Systems Interact with Mutual Respect in Classrooms
Bibliographic record
Abstract
This ethnographic and comparative study examines interactions between educational systems and mutual respect in classrooms. I define mutual respect as the work of intervening on those power asymmetries typically found in classrooms — both between teachers and students, and among students — by way of according children increased equality, autonomy, and equity. I partnered with four elementary schools, situated across two educational systems (i.e., Montessori and International Baccalaureate) and two national contexts (i.e., Washington, D.C. and Toronto). Through participant observation, interviews, video-cued multivocal ethnography, and document collection, I analyzed: 1) the ways in which educational systems design for mutual respect; 2) how teachers understand and manage mutual respect in practice; and 3) the experiences of teachers and leaders as they manage designs for mutual respect in practice. Overall, the findings from this study provide clearer understanding of how and why systems might design for mutual respect, and how and why approaches might differ across systems. What is more, the findings suggest that a system’s relationship with its environment can shape the trajectory of mutual respect within the designs of a system, the practical logics of teachers, and the social contexts of classrooms. This study contributes an analytic framework — informed by the literature and elaborated via empirical study — for describing, comparing, and reasoning about the relationship between educational systems and increased equality, autonomy, and equity for students in 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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.034 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| 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".