Perceived voluntary code legitimacy: Towards a theoretical framework and research agenda
Bibliographic record
Abstract
Abstract Increasingly within industries voluntary codes (standards) are being developed and subsequently used by firms to address social and environmental issues. On any particular issue multiple competing codes may be available for adoption by firms. Given a choice of codes, which ones will firms adopt? Building on existing institutional and economic research pertaining to voluntary codes this paper proposes a theoretical model as to why some codes are perceived as legitimate by firms and hence are widely adopted while others are not. This model proposes that, in addition to the role of the code's content, the characteristics of the adopting firm, and environmental factors, the origins of a voluntary code, including the characteristics of the developer creating it, the development process, and the opportunity for firms to engage in formalized ‘normative conversations’ regarding the code subsequent to its adoption, will influence whether potential firm adopters perceive the code as legitimating and hence decide to adopt it. Rather than code adoption simply reflecting institutional mimicry or a rational transaction by adopting firms this model suggests that both the creation and the maintenance processes surrounding codes play important roles in the perceptions of legitimacy and subsequent adoption of codes by firms.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.040 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".