Anthropologies of Cancer in Transnational Worlds
Bibliographic record
Abstract
Cancer is a transnational condition involving the unprecedented fl ow of health information, technologies and people across national borders. Such movement raises questions about the nature of therapeutic citizenship, how and where structurally vulnerable populations obtain care, and the political geography of blame associated with this disease. This volume brings together cutting-edge anthropological research carried out across North and South America, Europe, Africa and Asia, representing low-, middleand high-resource countries with a diversity of national health care systems. Contributors ethnographically map the varied nature of cancer experiences and articulate the multiplicity of meanings that survivorship, risk, charity and care entail. They explore institutional frameworks shaping local responses to cancer and underlying political forces and structural variables that frame individual experiences. Of particular concern is the need to interrogate underlying assumptions of research designs that may lead to the naturalizing of hidden agendas or intentions. Running throughout the chapters, moreover, are considerations of moral and ethical issues related to cancer treatment and research. Thematic emphases include the importance of local biologies in the framing of cancer diagnosis and treatment protocols, uncertainty and ambiguity in defi nitions of biosociality, shifting defi nitions of patienthood, and the sociality of care and support.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".