Applications of Social Network Analysis in Evaluation: Challenges, Suggestions, and Opportunities for the Future
Bibliographic record
Abstract
Abstract: As the use of social network analysis in evaluation continues to increase, it is important to understand how, when, and under what conditions social network analysis can add value to evaluation work. In this article, we describe how we have used social network analysis in various evaluation projects. Using the experience of one specific project, we highlight, in greater detail, some challenges we encountered in doing this work, relating to the need for stakeholders to understand the added value of social network analysis, the intricacies of data coding and cleaning, and how changes in the size and scope of the project can have great implications. Finally, we offer some practical suggestions for evaluators considering incorporating social network analysis into their work today, and identify opportunities where evaluators might use social network analysis in the future.
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.
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 | 0.328 | 0.305 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.024 | 0.033 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".