Bibliographic record
Abstract
Social network analysis takes as its starting point the premise that social life is created primarily and most importantly by relations and the patterns formed by these relations.Social networks are formally defined as a set of nodes (or network members) that are tied by one or more types of relations (Wasserman and Faust, 1994).Because network analysts take these networks as the primary building blocks of the social world, they not only collect unique types of data, they begin their analyses from a fundamentally different perspective than that adopted by individualist or attribute-based social science.For example, a conventional approach to understanding high-innovation regions such as Silicon Valley would focus on the high levels of education and expertise common in the local labour market.Education and expertise are characteristics of the relevant actors.By contrast, a network analytic approach to understanding the same phenomenon would draw attention to the ways in which mobility between educational institutions and multiple employers has created connections between organizations (Fleming et al., forthcoming).Thus, people moving from one organization to another bring their ideas, expertise, and tacit knowledge with them.They also bring with them the connections they have made to coworkers, some of whom have moved on to new organizations themselves.This pattern of connections between organizations, in which each organization is tied through its employees to multiple other organizations, allows each to draw on diverse sources of knowledge.Since combining previously disconnected ideas is the heart of innovation and a useful problem-solving strategy (Hargadon and Sutton, 1997), this pattern of connections -not just the human capital of individual actors -leads to accelerating rates of innovation in the sectors and regions where it occurs (Fleming et al., forthcoming).In this chapter, we begin by discussing issues involved in defining social networks, and then go on to describe three principles implicit in the social network perspective.We explain how these principles set network analysis apart from attribute-or group-based perspectives.In Section II we summarize the theoretical roots of network analysis and the current state of the field, while in Section III we discuss theoretical approaches to asking and answering questions using a network analytic approach.In Section IV we turn our attention to social network methods -which we see as a set of tools for applying network theory rather than as the defining feature of network analysis.In our concluding section we argue that social network analysis is best understood as a perspective within the social sciences and not as a method or narrowly-defined theory.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".