Bibliographic record
Abstract
Abstract Though critical realism has been featured in sociological debates about the philosophy of science, its relevance to methodological considerations, and especially to ethnographic scholarship, is quite limited. This chapter combines an extended case method approach to ethnography with a critical realist approach to comparison. Critical realism augments ethnographic comparison in two ways: 1) by showing that one can compare across both events and causal mechanisms due to ontological stratification; and 2) by considering the conjunctural and contingent nature of causality. However, critical realism’s emphasis on causality is also complicated by ethnographic research, which sheds light on the mutual causal relationship between structures and actors. This chapter, therefore, considers what critical realism has to offer ethnography and what ethnography, in turn, offers critical realism. It does so by comparing the experiences and beliefs of Somali refugee communities in Minneapolis and Toronto, who are contending with high rates of autism spectrum disorder and have forged epistemic communities united around an etiology, ontology, and treatment protocol that challenges mainstream science.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".