Bibliographic record
Abstract
The aim of this study is to assess the significance of social capital in a public organization according to two theoretical frameworks. Following the structural hole theory (Burt, 1992), a sparse social network enables employees to gain control and information benefits. According to the social capital theory (Coleman, 1988), a cohesive social network creates trust and an obligation to cooperate. The theories describe favorable outcomes of the opposite poles of social structure, but the discussion shows that the social capital might not be realized because of unfavorable contextual factors. Empirical findings indicate that a sparse ego network increases an employee's indirect control and that a dense work unit network increases trust in the democracy of decision making. The discussion suggests that a sparse social network might be most beneficial to a bureaucratic organization and that cohesiveness does not automatically induce commitment if it is not supported by favorable social norms. Unless prerequisites of social interaction are well secured, the organization faces the risk of having inadequate levels of social cohesion, which might impede the creation of social capital. In conclusion, the management is faced with the challenge of social liabilities arising from both social cohesion and the lack of it.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".