Design of Research for Systemic Design: Insights from an occupational safety study of sanitation workers in urban India
Bibliographic record
Abstract
This paper draws on a systemic intervention in faecal sludge management in the Indian urban context. The study was undertaken in two urban locations in the state of Tamil Nadu to gain a deeper and clearer understanding of desludging operators’ occupational practices and worker’s safety. The paper discusses the scope, method and approach that was conducted for the Need Assessment Study (NAS) to draw insights for the design of research for culturally diverse contexts. Ethnographic frameworks have been helpful for contextual inquiry; however, with complex systems, the multiple dimensions to be understood pose a significant challenge. Several questions emerge. What is the right sample size? What is the right mix of quantitative and qualitative? What impact would cultural plurality have on the research process? When to stop probing further while conducting research? Further consideration is given to whether complex systems require a nonlinear approach to conducting research and allow mixed methods. The design of research is acknowledged as a challenge. This case study draws upon broad considerations for conducting design research in pluralistic systems. Conducting and designing research with the awareness that complex social systems cannot be defined, mapped, or transformed without the participation of those whom the process and result will impact. The presentation considers the question of indigenous knowledge and how this could be leveraged.
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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 | 0.067 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.033 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".