Bibliographic record
Abstract
The focus, thus far, has been largely on civic participation; we turn in this chapter to two other critical dimensions of ‘social capital’ for Putnam, namely civic trust and shared norms. Indeed, it is the connection between participation and trust that lies at the core of social capital's unique contribution to the study of politics and society. As Pippa Norris and Ronald Inglehart comment: ‘The core claim of Putnam's account [of social capital] is that face-to-face … horizontal collaboration within voluntary organizations … promotes interpersonal trust’ (Norris and Inglehart, 2006, p. 2). While civic participation is the extent to which individuals join associations and can be measured by membership figures in voluntary associations as well as surveys of the general populace, civic trust is the degree to which people trust the generalized ‘other’ and is normally measured through public opinion analysis. The ‘shared norms’ that ‘attend’ trusting communities vary considerably in definition, as shall be discussed. At a minimum, Putnam explicitly argues for reciprocity and trustworthiness, but – as I shall argue – embedded in Putnam's theory is a much broader set of shared cultural norms that implies a thicker and more homogeneous kind of community than the minimalist definition might suggest. We begin by exploring the idea of ‘trust’ and incorporate the idea of shared ‘norms’ later in our analysis.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".