Divided We Stand: The Policy Bifurcation of Fields in the Aftermath of Critical Events
Bibliographic record
Abstract
The growing literature on fields and especially on field-configuring events have drawn attention to significant discontinuities in firm-level corporate social responsibility (CSR). It also revealed how exposure to, versus protection from, specific events yield different firm-level responses and therefore introduces substantial and sometimes persistent heterogeneity within initially homogeneous fields. We borrow and blend arguments from the literature on attention to theorize how changes in policy following particularly vivid or traumatic events like the Fukushima nuclear accident divide previously undifferentiated fields. We theorize that policy changes act as attention cues that heighten and/or hasten firms’ CSR efforts. We test these hypotheses using a difference-in-differences approach for two parallel natural experiments of that matched 206 firms from jurisdictions where nuclear policy changed in the immediate aftermath of the Fukushima event with peers operating in settings where national governments either withheld their prior policies (Experiment A) or had a moratorium already in place (Experiment B). Our results show robust field bifurcation effects of policy change for three out of four aspects of CSR. Firms respond to critical events not only by adopting policy-related initiatives but also by investing in types of CSR that are more versatile and apply beyond these critical events. Taken together our results suggest by accentuating CSR efforts by firms only in specific jurisdictions, policy changes bifurcate who responds, when, and how, thus introducing significant firm-level discontinuities and field-level heterogeneity in the aftermath of natural and man-made disasters.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".