Understanding connectivity: The parallax and disruptive-productive effects of mixed methods social network analysis in occupational science
Bibliographic record
Abstract
This article introduces social network analysis (SNA), a theoretical perspective accompanied by a set of methodologies, to occupational science. The convergence of SNA and occupational science is timely for both fields. By providing methodological approaches that flesh out a structural view of social networks, SNA measurements and mathematical terminology can effectively bridge the complexity of diverse interpretive frameworks used to understand occupational engagement and other constructs for humans as socially occupied beings. By focusing attention on the relationship of occupations to connectivity between agents, occupational science can make significant contributions to the ways in which the mattering or meaning of what people do with others nurtures the development and sustainability of social networks. We provide a brief history and roots of SNA in naturalistic observation, current terminology, and four widely used SNA research designs: egocentric, sociometric, sequenced, and two-mode. Drawing examples from our decade-long journey using SNA with narrative phenomenological conceptual frameworks, we illustrate how we used SNA with experience-near ethnographies to meet different objectives. In the discussion, we reflect on the parallax view created by the synergies between the disciplines and how the disruptive-productive effects that occur with mixing narrative phenomenology and SNA methods could address (mutual) methodological gaps that have seemingly limited conceptual development in the social sciences.
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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 | 0.078 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.012 | 0.022 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| 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".