Connecting Narrative and Social Representation Theory in Health Research
Bibliographic record
Abstract
According to narrative theory, human beings are natural story-tellers, and investigating the character of the stories people tell can help us better understand not only the particular events described but also the character of the story-teller and of the social context within which the stories are constructed. Much of the research on the character of narratives has focussed on their internal structure and has not sufficiently considered their social nature. There has been limited attempt to connect narrative with social representation theory. This article explores further the theoretical connections between narratives and social representations in health research. It is argued that, through the telling of narratives, a community is engaged in the process of creating a social representation while at the same time drawing upon a broader collective representation. The article begins by reviewing some of the common origins of the two approaches and then moves to consider a number of empirical studies of popular views of health and illness that illustrate the interconnections between the two approaches. It concludes that narratives are intimately involved in the organization of social representations.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.005 | 0.058 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".