Governance of dependency relationships in mandated networks
Bibliographic record
Abstract
Mandated service delivery networks have become common in both developing and developed worlds; however, our understanding of what makes these arrangements work is still limited. In this study, we draw upon existing business literature, specifically focusing on dependency relationships and resulting opportunism within mandated networks, a critical but often overlooked aspect. Further, within mandated networks characterised by limited autonomy and trust, ways in which network members navigate dependency relationships remain unexplored. We conduct a comparative case analysis, examining network arrangements within India’s National Health Insurance Programme. Based on our findings, we propose that the nature of interdependence among network members and the resultant dependency relationships impact the conduct of network members and, thereby, network performance. If the dependency relationships are not governed effectively, conflict bargaining and opportunistic behaviours get manifested. Contrary to expectation, network performance tends to be higher in jurisdictions where dependency relationships are effectively governed through hierarchical authority. These findings hold significant relevance; as mandated networks are created under the shadow of hierarchy but governed through clan or trust-based mechanisms.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".