Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.196 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it