Social Capital as Social Relations: The Contribution of Normative Structures
Bibliographic record
Abstract
This paper presents a framework for social capital that highlights the normative structures through which it is manifested. The primary focus is on the ways that norms structure the relationships in which social capital is embedded. To this end, we introduce four types of normative structures which condition social capital: market, bureaucratic, associative, and communal. A field site in Japan is used to illustrate how different aspects of social capital interact. This case analysis also serves to make an important distinction between the availability and use of social capital. The central arguments are that 1) social capital is organized in different ways by the normative structures in which it is embedded; 2) there are important interactions between these different aspects of social capital that are often overlooked by simpler frameworks; 3) a useful distinction can be made between available social capital and used social capital; 4) access to social capital can be used to analyze power relations; and 5) distinguishing different aspects of social capital makes areas visible that are overlooked by other understandings of social capital. We conclude by identifying the utility of our perspective for informing public policy and guiding future research.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".