Have a Go or Lay Low? Predicting Firms’ Rhetorical Commitment versus Avoidance in Response to Polylithic Governmental Pressures
Bibliographic record
Abstract
Abstract This study extends prior research on corporate political behaviour (CPB) and firms’ pursuit of political legitimacy in response to monolithic government pressures by developing and testing a framework for analysis of CPB in response to polylithic pressures. We suggest that traditional forms of CPB may be ill‐suited to polylithic governmental pressures, such as when firms need to navigate between conflicting home‐ and host‐country political worldviews and policies. We posit that in such complex political situations, firms will turn to a more subtle form of CPB (i.e., rhetorical commitment versus avoidance) as a hoped‐for solution to their international political legitimacy challenge. Our contingency perspective also highlights how geopolitical factors (i.e., whether governments of home and host countries are clearly aligned versus misaligned) will influence whether firms express their support for a home government’s foreign policy or avoid any such expression of support. We empirically test the predictive power of our framework by analysing how these political factors led Chinese firms to opt for rhetorical commitment versus rhetorical avoidance vis‐à‐vis the Chinese government’s Belt and Road Initiative (BRI). We conclude with a discussion of how our framework for analysis and our supportive findings can inform and extend research on CPB and political legitimacy.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".