Coping with conflicts in the co-production of solid waste management services: experience with a real-world lab in India
Bibliographic record
Abstract
Co-production of knowledge and services has been employed for citizen participation to address multiple complex challenges in various policy and practice fields. By analyzing the strategies used by a collaborative initiative called CANALPY to propel an innovative campaign for solid waste management in Kerala, India, we examine the conflicting undercurrents influencing the process of co-production of services. In particular, we look at the influence of a non-governmental player in supporting service provision by the local government and an examination of the conflicts and complex power dynamics that inform the behavior of various actors. A qualitative case study approach is employed to analyze the campaign design process, the strategies employed for inducing co-production among the citizens, and the diverging interests and power of different stakeholders. The findings elucidate how CANALPY, with relatively little political power, leveraged conditioned power to align stakeholders’ interests and mitigate conflicts to support the co-production of services. Through this empirical account, we intend to lay bare the varied expressions of multiple power differentials and show how, through epistemological convergence, CANALPY paradoxically reproduces entrenched power relations and provides space to subvert them.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".