Comfortably Uncomfortable: Unpacking the Microdynamics of Field Stability and Change
Bibliographic record
Abstract
In this paper, we examine how field settlements are formed over time by zooming in on the actions and reactions of field incumbents as they seek to make sense of and collectively respond to external pressures for change in their field. We illustrate this process through an in-depth case study of the staff and members of an international industry association as they attempted to deal with pressures to change current industry practices relating to water and climate change. Specifically, we show how field settlements that change a field’s “rules of the game” are constituted by the sequential and cumulative layering of increasingly committing interim agreements between incumbents (which we refer to as microsettlements), themselves facilitated or impeded by practices that help calibrate tension levels between them. Our process model of microsettlement outcomes and trajectories contributes to extant research by theorizing how the inner workings of field-configuring organizations and the composition and structure of field settlements shape field (re)formation processes, thereby illuminating new pathways of action for organizations seeking to tackle societal grand challenges in creative, substantive, and meaningful ways.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.042 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".