“Doing Good,” But for Whom? Board Political Ideology, Stakeholder Alignment, and the Strategic Alloc
Bibliographic record
Abstract
The discourse around climate change has become both polarized and politicized, as beliefs about its existence, causes, and severity have become markers of partisan affiliation. As such, a full understanding of corporate strategies toward addressing climate change requires paying attention to the political beliefs and ideologies of key corporate stakeholders. The primary goal of this study is to examine the influence of board political ideology and stakeholder attributes on how firms go about reducing their greenhouse gas (GHG) emissions. We argue that the political ideology of a firm’s board will carry a main influence over the firm’s pursuit of a pro-climate strategy. However, how firms manage the reduction of their GHG emissions will depend on the ideology of the local communities where their facilities are located and the degree of civic organizations in these communities. As a consequence of pursuing “doing good” for those stakeholders with whom the board is more aligned, however, areas that are objectively subjected to greater climate risk will go overlooked. We empirically test our hypotheses using a sample of 18,412 observations from 2,700 facilities in the United States, finding strong support for our hypotheses.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".