Representations of Science and Scientists in the Television Sitcom “Friends”: Contributions to the Public Understanding of Science
Bibliographic record
Abstract
The television situation comedy “Friends” with the character Ross as a scientist is notable both because it’s one of the top-ranked shows in its category of all time and because it’s currently one of the top viewed situation comedies on Netflix. Much of the research on the influence of entertainment television on attitudes/understanding about science and scientists is on science-centric shows (e.g., CSI, The Big Bang Theory) but not on shows in which science is present but not central (e.g., Friends or Last Man Standing). The viewing, and re-viewing, of shows on Netflix during binge watching means that the tropes about science and scientists presented in the show “Friends” are foregrounded and more persistent for viewers. This presentation presents an analysis of the representations of science and scientists in the show and discusses the ways in which these may influence the public’s understanding of them. This analysis uses the analytic tools and framework presented previously, drawing on descriptions of NOS & Science/Engineering practices from NGSS documents, inquiry and investigation approach tools from other studies, and from descriptions of the social practices of science.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| 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".