A sociogram is worth a thousand words: proposing a method for the visual analysis of narrative data
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
This article proposes an innovative method for the visual analysis of narrative data that involves three steps: transforming narrative data into relational data, creating two-mode networks displayed with graph optimization algorithms derived from social network analysis (SNA), and visually analyzing sociograms. We argue that understanding how actors and their opinions constitute a network-like structure opens up promising avenues for interpreting data. This approach provides powerful data visualization that facilitates inductive identification of the underlying structure of narrative data. It also reveals the complexities of the links between differently positioned actors in a structure that a personal attribute-based analytical method might overlook. Lastly, it can be productively combined with other quantitative and qualitative methods to make sense of narrative data.
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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 | 0.138 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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