Muddling through Political and Economic Tensions:A Territorialized Conception of Political CSR
Bibliographic record
Abstract
This article draws on the political CSR literature to investigate how an organization responds to political and economic tensions through its corporate community involvement (CCI). It is based on the longitudinal case study of a hybrid organization in the space industry, which has engaged in relations with the local community over the last 50 years and has gone through significant change. Through this exploratory study, we make two main contributions. Firstly, we extend the political CSR literature by showing that the current transformation of CSR is not only the result of a movement of the private corporation into the public (political) sphere, but also the consequence of a converse movement of public (political) actors into the economic sphere. The blurred frontiers between the economic and political spheres theorized by political CSR are then the result of this dynamic and nonlinear movement, which increasingly generates hybrid organizations. Secondly, this paper shows that in order to understand how CSR is shaped it is necessary to recognize the contingencies of the local context in which a firm operates. This finding critically challenges the de-territorialized approach of political CSR which implicitly leads to the conclusion that CSR follows a universal template, and instead calls for more research on the role of the local context (i.e. the community) in shaping CSR.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.049 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".