Bibliographic record
Abstract
This article explores some relational mechanisms that may facilitate strong inter-organizational alliances, and the associated trust, in collective action fields. It departs from mainstream research on trust by focusing on organizations rather than individuals. The article focuses in particular on three mechanisms: embeddedness (i.e. the role of individual members’ multiple involvements as facilitators of sustained inter-organizational exchanges), familiarity (the role of previous interactions in facilitating cooperation and thus generating trust), and brokerage (the role of trusted leaders in bringing different organizations together). These mechanisms may affect trust creation in two different ways: by facilitating dyadic alliances between pairs of actors and by facilitating actors’ incumbency of the same network position, regardless of being directly connected. Illustrations come from data on environmental groups in Milan in the mid 1980s, civic organizations active on various public issues in Glasgow and Bristol in the early 2000s, and organizations active on the urban environment in Cape Town in the early 2010s. While the exercise is exploratory in nature, the relational mechanisms identified may represent some of the building blocks for a systematic explanation of alliance building and trust emergence. Moreover, data from urban polities with different levels of democratic consolidation and cleavage pacification illustrate the importance of adding a comparative element to our explorations of the relationship between trust and collective action.
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".