How many flukes do you need? Stories on breast cancer at the Ambassador Bridge
Bibliographic record
Abstract
1 in 8 Canadian women are diagnosed with breast cancer and incidence is rising. Only 5 to 10% of cases are genetic though when consulted, many women seem believe it is the most important factor in developing the disease. The scientific evidence increasingly tells us that up to 70% of breast cancers are linked to environmental exposures. Air pollution currently has the attention of many breast cancer researchers with multiple studies finding elevated incidence among women in high levels of exposure. My research with women workers at the Ambassador Bridge in Windsor, ON, the busiest border crossing in North America, where over 20,000 transport trucks and other vehicles cross each day, and a workplace where women are getting breast cancer at rates 16 times higher than the rest of the county, explores the layers influencing understandings, decisions, meaning-making and perceptions of environmental breast cancer risks in all their richness. The complexity of intersections of biological and sociological factors for breast cancer risks reveals this is not simply a public health issue, but also one of environmental justice. Kleinman’s cultural model of illness guides the identification of the subject in this study and how subject location influences understandings, interpretations and use of knowledge from different sources (e.g., personal and vicarious experiences, popular and social media, authoritative sources) in creating narratives and discourses of breast cancer risk. By analyzing in-depth individual interviews with 25 women workers from the Bridge, a story emerges about how women construct their narratives for risks for breast cancer by incorporating varying sources of information and making decisions about using that information based on “what really matters” to them. Personal experiences and interpretations of risk are intertwined, bringing together the biomedical, cultural and personal.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".