Hollywood raising awareness of smoking-related diseases: can it proactively counteract the impact of smoking in movies?—the final mission of Star Trek’s Mr Spock
Bibliographic record
Abstract
On 27 February 2015, generations of fans of Star Trek lost one of its iconic characters. That day, Leonard Nimoy, also known as Mr Spock on the starship Enterprise , died as a consequence of chronic obstructive pulmonary disease (COPD). What many people may not know about Leonard is that since his COPD diagnosis in 2013, despite giving up smoking about 30 years earlier, he became a Star -educator on his own disease. He reached out to his fans on Twitter and in interviews, discussing the burden of COPD, motivating people to quit smoking and highlighting his personal struggles while living with COPD. For a good reason: COPD is now the third leading cause of death in the world and is primarily caused by smoking exposure, but it is still one of the diseases that are least known by the general audience. After his death, his daughter and her husband continued his mission by announcing the film ‘Remembering Leonard—His Life, Legacy and Battle with COPD’, a documentary that will aim to educate viewers about COPD by using personal stories of Leonard, as well as information about treatments. It is expected to be launched in 2016. 1 Ironically, many people may actually have initiated smoking because of television and Hollywood smoking influences. 2 Therefore we were wondering whether we could also use movies and famous actors to educate their viewers on the health risks of smoking (i.e. the risk of lung cancer, cardiovascular disease and COPD) and actively involve Hollywood in helping them to quit—or never start—smoking?
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.021 | 0.030 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".