Why Meta-Organizations Matter: A Response to Lawton et al. and Spillman
Bibliographic record
Abstract
In a recent issue in this journal, Lawton et al. and Spillman argue for the importance of studying trade associations, also referred to with the broader term meta-organization. They discuss why meta-organizations matter and why more research is needed on the topic. We fully concur with the authors that meta-organizations constitute an inflating, diverse, and undeniable phenomenon of collective action among organizations and that collective scholarly efforts are necessary to improve our understanding of meta-organizations in their multiplicity. In this article, we shed some light on a body of work already investigating the matter. They constitute what we call the “European School” of meta-organization. We show the relevance of this recent European work for the US–UK-oriented trade association research and aim to bridge the gap between these research traditions by proposing a common research agenda on key topics of resources, forms’ differentiation, coopetition, and their role in sustainability governance.
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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 | 0.043 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.053 |
| Scholarly communication | 0.019 | 0.048 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.035 | 0.045 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".