Settlement Constellations and the Dynamics of Fields Formed Around Social and Environmental Issues
Bibliographic record
Abstract
Firms are increasingly responding to social and environmental issues in highly complex and heterogeneous organizational fields that transcend national boundaries. Yet, we still have a limited understanding of how these fields are structured and the implications of structural variation on how issues are addressed over time. We advance theory in this area by arguing that issue fields are characterized by varying settlement constellations that structure these fields. We develop a typology of three settlement constellations—unified, fragmented, and bifurcated—and describe their impact on field structure and the challenges they raise for addressing field-defining issues. We then theorize the evolution of fields with different settlement constellations and explain how and why constellations are sustained over time as well as when they may change. Our paper helps advance theory on organizational fields, private regulation, and firm responses to social and environmental issues. More broadly, our paper highlights the unique position of organizational and institutional scholars to examine complex social and environmental issues, or “grand challenges.”
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".