Waste Pickers in Istanbul: on a Pathway towards Formalization? A qualitative case study exploring opportunities and challenges following the adoption of new regulations on waste picking and the possible role of cooperatives in a transition to formality
Bibliographic record
Abstract
Waste picking is a means of survival for thousands of individuals in Istanbul and together the city’s waste pickers make a substantial contribution to the recycling sector. However, they have struggled to gain recognition as recycling providers and have suffered from legislation criminalizing their source of livelihood. 2022 marks a shift where a new regulation was adopted, allowing waste pickers to apply for permission to work as independent waste collectors within their municipalities. This study seeks to gain an understanding of how this regulation has been received and applied in practice and what barriers to formalization that may be persistent. It also explores to what extent waste pickers in Istanbul have joined forces and been able to organize. The findings draw on interviews conducted with key informants.The study reveals that the regulation has several components making it largely exclusionary by design and that it is perceived to offer limited benefits to those meeting its eligibility criteria. The findings also offer insights from a newly established cooperative in Istanbul, and discusses the potential of an alternative route with cooperatives as key players in advancing the working conditions of waste pickers.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".