Removing Rose-Tinted Glasses: Uncovering the Dark Side Effects of Cross-Sector Partnerships
Bibliographic record
Abstract
While cross-sector partnerships (CSPs) are widely celebrated for addressing societal challenges, their potential negative effects on communities, environmental ecosystems, and society at large are often overlooked. This oversight obscures our awareness and understanding of recurring patterns, not only in the various types of negative societal effects, but also in the mechanisms through which CSPs may generate these effects, and the partnership-related antecedents. Through a qualitative meta-analysis of 39 studies we synthesize existing empirical evidence and examine the negative societal effects of CSPs. Our analysis reveals the what (effects), how (mechanisms), and why (antecedents) of these “dark side” effects, thereby linking societal, intervention, and organizational perspectives on tackling complex societal challenges. We discuss the implications of our analytical framework for CSP research, practice, and the broader study of organizations’ dark side.
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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 | 0.115 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".