Local Grounding of Transnational Private Governance Authority: Translation, Contestation, Legitimation and Communities of Practice
Bibliographic record
Abstract
Scholars emphasise the constitutive ambiguity of transnational private standards and the importance of global-local interactions in their implementation. Yet how this ambiguity and these interactions shape the legitimation of transnational private governance, especially in the norm formation phase, remain open questions. The conceptual metaphor of ‘grounding’ offers a promising perspective on these questions. This article conceptualises the grounding of transnational private governance in terms of practices of translation by which transnational standard-setting is grounded in receptive local contexts; practices of contestation by which it runs aground on local resistance; and communities of practice that shape the normative grounds for legitimate standard-setting authority. An illustrative example of local Colombian reactions to the development of the global social responsibility guide ISO 26000 suggests that a basic principle of private standardisation, that standards are developed through a consensus process in which all concerned interests are effectively represented, is not as important to the legitimation of standards as many suppose, and that membership in two overlapping communities of practice—standardisation and corporate social responsibility—explains why actors legitimise standard-setters that do not fulfill a legitimacy criterion they purport to consider crucial.
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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 | 0.037 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.101 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| 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".