Use of social network analysis to map the social relationships of staff and teachers at school
Bibliographic record
Abstract
Understanding the pre-existing social relationships in a setting is vital in health promotion, not only for understanding important people to get 'on side' with an intervention but also for appreciating how the intervention itself might change social structures. Social network analysis is a method for capturing the complexity of social relationships that has not been used widely in health promotion research. We present the results of an application in a high school. We characterize the school in terms of the density of relationships and the centrality of particular staff and teachers. We illustrate how simply being well-known or being nominated by lots of others as a person to turn to (a concept reflected in a person's degree centrality score) is not always the best guide for whom to select as an intervention champion. Indeed, for many interventions, a person's strategic connection to the most marginal people in a community, school or workplace could be the most important criteria (a concept better reflected by a person's betweenness centrality score). Given the ease of survey administration and the high yield in terms of analytic insight, we recommend that social network analysis be used more routinely in health promotion intervention design and evaluation.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".