Social disclosure, legitimacy theory and the role of the state
Bibliographic record
Abstract
Purpose The principal objective of this paper is to expand the scope of legitimacy theory (LT) through a detailed analysis of the links that exist between the legitimising strategies of firms and the characteristics of the political environment in which they are developed. Designs/methodology/approach A discourse analysis was performed on the social and environmental disclosure (SED) of a multinational in the automotive sector with an established presence in Spain, in the context of the relational dynamics between the firm/society/state. Different channels of information were compared to capture both the official discourse as represented in the annual reports of the multinational and the discourse of employees and the State as represented in the media. Findings The results of the research show that the firm under study used SED strategically to legitimise a new production process through the manipulation of social perceptions, and that this strategy was supported implicitly and explicitly through ideological alignment with the State. Research limitations/implications Despite a widely‐held assumption of a pluralist political context, the State is presented here as aligning itself with corporate management as opposed to the welfare concerns of employees. Thus, future research calling for regulation of SED should preface such calls with consideration of the orientation of the State. Originality/value In contrast with the dominant approach to LT that considers the relationship of the firm with its stakeholders, the present study widens the scope of LT to consider the interplay between firm legitimating strategies and state support for such strategies.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.048 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".