Methods for Analysis of Social Networks Data in HRD Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it