Bibliographic record
Abstract
The last decade has seen many of the 'community' concepts in health (community empowerment, community capacity) replaced by 'social' concepts (social capital, social cohesion). The continuous re-labelling of roughly similar phenomena may be a necessary stratagem to attract attention to the economic and power inequalities that arise from undisciplined markets. Social concepts also have an advantage over community ones by directing that attention to higher orders of political systems. The latest construct being wielded by health practitioners, researchers and policy-makers are the twinned concepts of social inclusion and social exclusion. These represent a conceptual sophistication over social capital and social cohesion. Like their predecessors, however, there are risks in their adoption without a critical examination of the premises that underpin them. For example, how can one 'include' people and groups into structured systems that have systematically 'excluded' them in the first place? The cautions expressed in this article do not dissuade use of the concepts. Their utility, however, particularly at a time when not only inequalities, but also their rate of growth, is increasing, requires careful questioning.
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.
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 | 0.027 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.014 | 0.178 |
| Scholarly communication | 0.027 | 0.035 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".