Keynote Address: Empirically Exploring Narrative Productions of Meaning in Public Life
Bibliographic record
Abstract
Because socially circulating stories are key vehicles producing shared meaning in globalized, mass-mediated, and heterogeneous social orders, it is important to understand how some stories – and only some stories – can be evaluated by large numbers of people as believable and important. How do stories achieve widespread cognitive and emotional persuasiveness? I argue that understanding narrative persuasiveness requires a cultural-level analysis examining relationships between story characteristics and two kinds of meaning: Symbolic codes which are systems of cognitive meaning and emotion codes which are systems of emotional meaning. Persuasiveness of narratives is achieved by using the most widely and deeply held meanings of these codes to build narrative scenes, characters, plots, and morals. I demonstrate my argument using the example of the codes embedded in the social problem story of “family violence,” and I conclude with some thoughts about how sociologists might approach the production of socially circulating stories as topics of qualitative research and why there are practical and theoretical reasons to do so. My central argument is that examining relationships between cultural systems of meaning and the characteristics of narratives is a route to understanding a key method of public persuasion in heterogeneous, mass-mediated social orders
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How this classification was reachedexpand
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".