Rogue Resistance: Sidestepping Isomorphic Pressures in a Patchy Institutional Field
Bibliographic record
Abstract
My research suggests that organizational fields are patchy and uneven. This patchiness allows organizations at the margins of fields to sidestep pressures for conformity. As a case study, this paper examines the private school field in Toronto, Canada. Data come from interviews and site visits at 60 Toronto private schools. My findings suggest that Toronto’s private school field is segmented, incorporating diverse private school forms, including elite, religious, and ‘rogue’ (non-elite, non-religious) schools. Within one subfield – small rogue private schools – a high degree of heterogeneity exists. These findings suggest a nuanced conception of institutional fields, with more attention to organizational agency, multiple field logics and diversity among organizational forms. This paper examines how organizations at the margins of fields are able to evade pressures for conformity, and how a heterogeneous organizational field can also be comprised of clusters of homogeneity.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".