The Architecture of Influence: Meta-organizations and The Transformation of Institutional Fields
Bibliographic record
Abstract
In this study, we examine interactions between a mining industry association, its member companies and various SMOs (defined broadly) in an effort to better understand how such interactions come to shape industry-level corporate practices over time. Based on an in- depth case study of an international mining association, focused on enhancing corporate social responsibility (CSR) practice standards in the industry, and its interactions with 17 different SMOs, this study highlights the processes whereby a network of interlinked meta-organizations, comprised of industry associations, corporations, social movement organizations, intergovernmental organizations and standard setting organizations help shift corporate practices towards more or better sustainability in the industry. Our study shows that processes of engagement between actors are accomplished in three key stages – issue raising, issue defining and issue mainstreaming. These processes in turn are mediated by three distinct dynamics: transforming information into intelligence, bargaining for legitimacy, and resource availability. This study contributes to the literature on how SMOs and corporations interact and sheds light on the role played by meta- organizations in the shaping of institutional fields.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".