Bibliographic record
Abstract
We began this book by considering two different definitions of ‘social capital’; the instrumental, aggregative and functionalist definition of ‘capital’ (participation and trust) provided by the ‘American’ school (Coleman and Putnam), versus the more critical, historical perspective (networks and resources) of the ‘European’ school (Gramsci and Bourdieu). According to the former school of thought, investment in social capital is apolitical (since it exists outside the realm of the state), functional (since it serves larger ends), aggregative (since it is simply the sum of the number of individual decisions to connect) and positive (for democracy and individual well-being). As individuals choose to increase the number of connections in their community, higher levels of trust, solidarity and generalized reciprocity will result, and these can be quantitatively measured; in turn, such increased connectedness will result in better neighbourhoods, greater economic prosperity, more health and happiness for individuals and stronger democracies. It is for these instrumental and aggregative reasons that social capital and social connectedness are seen as largely positive by Coleman and Putnam. For all the emphasis on civic society and community in Putnam's thesis, at the end of the day the central units of analysis of this ‘capital’ are, in essence, the individual (whose interests, ‘rightly understood’, are being served by increasing cooperation) and the American nation (the democratic health of which depends upon the ‘civic culture’ in which it is rooted and the degree to which it is unified).
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.041 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".