Social capital, population health, and the gendered statistics of cardiovascular and all-cause mortality
Bibliographic record
Abstract
Scholars in the field of population health need to be on the constant lookout for the danger that their tacit ideological commitments translate into systematic biases in how they interpret their empirical results. This contribution illustrates this problematic by critically interrogating a set of concepts such as tradition, trust, social capital, community, or gender, that are routinely used in population health research even though they carry a barely acknowledged political and ideological load. Alongside this wider deconstruction of loaded concepts, I engage critically but constructively with Martin Lindström et al.'s paper "Social capital, the miniaturization of community, traditionalism and mortality: A population-based prospective cohort study in southern Sweden" to evaluate the extent to which it fits with other empirical findings in the extant literature. Taking as a point of departure the intriguing finding that social capital predicts cardiovascular and all-cause mortality only for men, but not for women, I argue that future research on the nexus of social capital, health, and mortality needs to frame gender not only as a demographic and statistical variable, but also as an ontological conundrum and as an epistemological sensibility.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".