Street-level collaboration: perception, power, and politics on the frontlines of collaboration
Bibliographic record
Abstract
Purpose To examine the implementation processes and outcomes of collaborative governance initiatives through the lens of bureaucratic politics. Design/methodology/approach An in-depth single case study research design with 28 embedded cases to study the implementation of a collaborative governance initiative. This paper uses the analytical technique of process tracing to explicate necessary and sufficient conditions to uncover causal mechanisms and confirm descriptive and causal inferences. Findings This study finds that when street-level bureaucrats perceived the collaborative initiative as a health intervention (and not as a collaborative initiative), it resulted in low levels of stakeholder participation and made the collaborative initiative unsuccessful. This paper finds that bureaucratic politics is the causal mechanism that further legitimized this perception resulting in each stakeholder group avoiding participation and sticking to their departmental siloes. Research limitations/implications This is a single case study about a revelatory case of collaborative governance implementation in India, and findings are analytically generalizable to similar administrative contexts. Further research is needed through a multiple case study design in a comparative context to examine bureaucratic politics in implementing collaborative initiatives. Practical implications Policymakers and managers need to carefully consider the implications of engaging organizations with competing institutional histories when formulating and implementing collaborative governance initiatives. Originality/value This study's uniqueness is that it examines implementation of collaborative governance through a bureaucratic politics lens. Specifically, the study applies Western-centric scholarship on collaborative governance and street-level bureaucracy to a non-Western developing country context to push the theoretical and empirical boundaries of key concepts in public administration.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".