Miscompromises and Their Consequences in Organizations
Bibliographic record
Abstract
Current research has made considerable progress in understanding how multiple worldviews settle in the form of compromises, notably through the lens of French pragmatism sociology’s economies of worth (EW). However, we know little about how and why compromises crumble, break and go forgotten over time. This is concerning, given the pluralistic nature of our polarized area where antagonistic visions conflict around contentious social issues. Therefore, in this paper we introduce the idea of miscompromise, defined as weak and unstable compromises that do not hold in the long term, but nonetheless impact organizations as an agreed framework for collective decision. Drawing from a qualitative study of a fourteen-month project to transform clinical, organizational and administrative practices in order to improve the performance of care deliveries in a health care organization, our study shows what makes miscompromises ephemeral in the first place, and how this constructed fragility leads them to be challenged, ignored, and ultimately rejected over time. Our research contributes to connect EW's pragmatic local perspective to broader issues of power or ideology documented in the institutionalist literature. On a more practical level, our focus on “bad compromise” also contribute to building more robust consensus making processes more likely to sustain collective action, especially around contentious social issues where multiple antagonistic worldviews compete and clash.
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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 | 0.028 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.021 | 0.068 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.004 | 0.006 |
| 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".