Toward a critical examination of social capital within leisure contexts: From production and maintenance to distribution
Bibliographic record
Abstract
Perhaps because of the normative foundations upon which our field was built, leisure researchers have focused on social capital at the group level with a particular emphasis on how communities of interest develop and more or less maintain social capital as a collective asset. In so doing, they have tended to concentrate on the positive externalities associated with social capital production. This approach to examining social capital in leisure contexts ignores the egocentric properties of social capital and implies incorrectly that social capital can be appropriated equally by all members of a social network. While social capital does represent resources embedded in social relations, the access and use of such resources ultimately reside with the individual. The uncritical acceptance within our field that social capital is a positive dimension in building community capacity fails to appreciate that members of a social network have differential access to social capital by virtue of their social position within that network. The individual returns of social capital are often (maybe even usually) distributed unevenly. With this recognition, the author calls on leisure researchers to focus on inequalities in access to social capital that result from what Lin (2001) referred to as a capital deficit or return deficit. Correspondingly, leisure researchers are challenged to pay greater attention to what Foley, Edwards, and Diani (2001) termed “use‐value,” that is, how appropriable social capital really is. Only by concentrating on the actual distribution of social capital can leisure researchers begin to de‐essentialize the relational ties developed in leisure contexts and the benefits accrued through them.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.007 | 0.051 |
| Scholarly communication | 0.015 | 0.029 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".