Bibliographic record
Abstract
Understanding the unique challenges facing vulnerable communities necessitates a scholarly approach that is profoundly embedded in the ethnographic tradition. Undertaking ethnographies of communities and populations facing huge degrees of inequality and abject poverty asks of the researcher to be able to think hard about issues of positionality (what are our multiple subjectivities as insider/outsider, knowledge holder/learner, and so on when interacting with vulnerable subjects, and how does this influence the research?), issues of engagement versus exploitation (how can we meaningfully incentivize participation in our studies without being coercive/extractive, and can we expect vulnerable subjects to become deeply in research design/data collection, and so on when they are so overburdened already?), and representation (what are the ethics of representing violence, racism, and sexism as expressed by vulnerable respondents? What about the pictures we take and the stories we tell?). Through the discussion of our research on the behavioral patterns, socialization strategies, and garbage processing methods of informal waste pickers in Argentina and Mexico, we ask ourselves, and through this exercise, seek to shed light on the broader questions of how can we engage in ethnographies of vulnerable communities while maintaining a sense of objectivity and protecting our informants? Rather than attempting to provide a definite answer, we provide a starting point for scholars of resource governance interested in using ethnographic methods for their research. We highlight the challenges we’ve faced in studying cartoneros in Buenos Aires (Argentina) and pepenadores in León (Mexico) and engage in a self-reflective discussion of what can be learned from our struggle to provide meaningful, engaged scholarship while retaining and ensuring respect and care for the communities we study.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".