009 OP: A SOCIOGRAM IS WORTH A THOUSAND WORDS: PROPOSING A METHOD FOR THE VISUAL ANALYSIS OF NARRATIVE DATA
Bibliographic record
Abstract
This presentation proposes and showcases an innovative method for the visual analysis of narrative data. This method rests on three steps: the transformation of narrative data into relational data, the use of graph optimization algorithms derived from social network analysis (SNA), and, finally, the visual analysis of the resulting sociograms. This method was developed and pilot tested in the context of a research project about stakeholders' views on the strengths and problems of Quebec's health care system, and the solutions needed to increase its performance and sustainability. SNA is a transdisciplinary methodological approach focused on understanding the structure of the relations that connect different elements. The scope of its application is very wide, from understanding the structure of molecular interactions and disease transmission in epidemiology to the analysis of kinship structures and community organization in anthropology. Although SNA has a long tradition in social sciences, its mainstream acceptance is recent. This presentation begins with a brief summary of the specificity, origins and evolution of SNA tools, followed by a discussion on how the reliance on relational analysis differentiates SNA from other paradigms used in qualitative and quantitative analysis. It then provides an overview of the narrative data analysis method we developed and illustrates it via a case study on different perspectives of the Quebec health care system. Lastly, we outline the originality, potential and applicability of using SNA-based methods to analyse narrative data collected in qualitative health research. We will argue here that examining how actors and their opinions constitute a network-like structure offers promising ways of interpreting data. In our research, the use of this method presented two main advantages. It provided powerful data visualization that facilitated the inductive identification of the underlying structure of our data. It also revealed the complexities of the links between differently positioned actors in the Quebec health care system that a personal attribute-based analytic method would have overlooked.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".