Resources, Knowledge and Influence: The Organizational Effects of Interorganizational Collaboration*
Bibliographic record
Abstract
ABSTRACT Inter‐organizational collaboration has been linked to a range of important outcomes for collaborating organizations. The strategy literature emphasizes the way in which collaboration between organizations results in the sharing of critical resources and facilitates knowledge transfer. The learning literature argues that collaboration not only transfers existing knowledge among organizations, but also facilitates the creation of new knowledge and produce synergistic solutions. Finally, research on networks and interorganizational politics suggests that collaboration can help organizations achieve a more central and influential position in relation to other organizations. While these effects have been identified and discussed at some length, little attention has been paid to the relationship between them and the nature of the collaborations that produce them. In this paper, we present the results of a qualitative study that examines the relationship between the effects of interorganizational collaboration and the nature of the collaborations that produce them. Based on our study of the collaborative activities of a small, nongovernmental organization (NGO) in Palestine over a four‐year period, we argue that two dimensions of collaboration – embeddedness and involvement – determine the potential of a collaboration to produce one or more of these effects.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".