Digitalization of Solid Waste Management
Bibliographic record
Abstract
Digital interventions in urban governance are frequently viewed in a binary light. They are either lauded as game changers or criticized as being completely vacuous projects. Studying the ongoing implementation of a project on digitalizing the solid waste management (SWM) system in Mangaluru, this paper presents a new way of looking at digital infrastructure that is not a zero-sum calculation. The paper makes three arguments: (i) digitalization brings in several new private players to urban governance, who end up complicating an already fragmented system, (ii) digital solutions adopted are neither suited to the labor-intensive working conditions of the SWM sector nor are they sufficiently deployed to bring effective change, and (iii) the idea of a one-stop online grievance redressal system as a game changer is misplaced since it does not improve the trust deficit among the residents. Through highlighting these issues, the paper presents the possibilities of understanding digital interventions as evolving aspects of cities, where new systems do not completely replace the old systems but create contradictions, revealing gaps for the local governments to improve on.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".