Bibliographic record
Abstract
Science studies have long been concerned with the complex interrelationship between scienti?c research and popular culture’s interpretations and reconstructions of scienti?c ?ndings (Kember 2003; Lancaster 2003; Penley 1997, among others). Disparities between the two are often presented as popular culture’s misinterpretation or misrepresentation of scienti?c facts; however, in this essay I argue that a more theoretically lucrative approach understands these con?icts as complex social and cultural negotiations over epistemological boundaries between scienti?c and popular cultures. Understanding such differences is tremendously important in mediated societies where scienti?c research is mostly understood through its representation in the popular culture. In this paper, I examine what is at stake in popular representations of scienti?c research and how the popular culture is often seen as threatening to the epistemic boundaries of scienti?c culture. Using the recent controversy over The Oprah Winfrey Show’ s presentation of controversial medical practices as a case study, this essay examines how distinctions between scienti?c and popular ways of knowing are constructed, represented and managed. I argue that scienti?c knowledge should be viewed as a complex and often con?icted cultural discursive practice that signi?es boundary negotiations between scienti?c and popular cultures.
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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 | 0.032 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.014 | 0.063 |
| Scholarly communication | 0.023 | 0.032 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".