Methods for Analysis of Social Networks Data in HRD Research
Bibliographic record
Abstract
The Problem Many phenomena in human resource development (HRD) research unfold in the social context. Most of the variables HRD researchers study, even if these are individual-level variables, are inevitably affected by the formal and informal network of actors in which an individual finds oneself. This relational influence is commonly ignored in studies of performance, learning, change, and other questions. The Solution Social networks analysis (SNA) is a methodology that makes it possible to take the relational aspect into account. It allows researchers to model the social capital of an actor and examine how connectivity and position in the network interacts with or influences important outcomes. The Stakeholders Researchers in the field of HRD will be able to uncover a wealth of new information and gain a new perspective by including SNA in their toolkit. This article provides the researchers with an introduction to the methods and provides suggestions for their application.
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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 | 0.052 | 0.210 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.018 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".