Categorical Cleaning: An exploratory study of sustainability induced divestitures, 1992-2010
Bibliographic record
Abstract
Sustainability has emerged as an increasingly important category of concern. Reflecting this shift, organizations are being evaluated on the basis of sustainability criteria. Firms in particular may find themselves rated as unsustainable because of their portfolio of businesses. I theorize that when confronted with negative sustainability ratings, rather than decoupling their unsustainable practices symbolically or displacing them substantively, firms might instead choose to divest them altogether. Seen from this perspective, firms achieve sustainability by dissociation. Using an unbalanced panel of diversified firms from 1992 to 2010, I test whether negative sustainability ratings are related to divestitures, and whether the strength of this relationship is moderated by the extent to which firms are targeted by shareholder activists, and whether or not firms engage in sustainability reporting. Drawing on my analysis, I propose a cultural perspective on organizational boundaries, as well as implications for divestitures research, categorization theory, and sustainability research.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".