Same but Different: Meta-Organization and Collective Identity Dynamics
Bibliographic record
Abstract
This article analyzes how a meta-organization (M-O) can shape a coherent collective identity over time. Previous foundational work on identity formation in M-Os has provided fragmented but insightful ideas on several activities that this process entails. However, we currently lack a dynamic, integrative, and empirically supported model that demonstrates how these activities interrelate to shape a coherent collective identity over time. Using an in-depth case study of an association of cider producers in Québec (Canada) over a 23-year period, we develop a model of collective identity dynamics, in which an M-O plays an orchestrator role that is both dual and continuous. On the one hand, an M-O balances the internal identity claims of its organizational members through alignment and differentiation. On the other hand, an M-O builds an externally coherent identity by assembling and positioning legitimacy among institutional actors. Our paper provides new insights into activities performed by an M-O during identity creation by analyzing whether this process includes both organizational and institutional actors, thereby reinforcing the intermediary nature of an M-O. Furthermore, it contributes to the collective identity dynamics literature by elaborating the stabilizing role of a bounded organization in collective identity dynamics at the interorganizational level.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".