From Animosity to Affinity: The Interplay of Competing Logics and Interdependence in Cross‐Sector Partnerships
Bibliographic record
Abstract
Abstract Drawing on and extending institutional logics and resource dependence theories, this paper posits that for cross‐sector partnerships to survive, organizations need to share compatible institutional logics, but depend less on each other's resources. Asymmetrical cross‐sector partnerships may lead to a breakup if organizations are forced to operate under incompatible institutional logics. The findings of this study show that the challenges posed by incompatible logics of partners could be mitigated by the degree of resource interdependence between organizations. Capturing the effects of context and transactions on the actors’ strategic behaviour, the findings, based on a dataset of project‐level partnership ties between 1312 organizations in the carbon‐offset market, support these hypotheses. The paper concludes by discussing implications of organizations' responses to keep acting under or reinterpreting existing institutional logics in asymmetrical cross‐sector relationships.
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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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.012 |
| 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".