Bibliographic record
Abstract
In February 2014, MPs in the UK House of Commons voted in favour of a law making it an offence to expose children to tobacco smoke in private vehicles. This decision followed the adoption of similar rules in five other countries, as well as six US states, nine Canadian provinces and every state of Australia. While increasingly widespread, this policy initiative remains controversial. In the UK, the House of Commons vote was framed as a choice between supporting the measure in order to protect children’s health and rejecting it as practically and philosophically problematic (Mason, 2014). Perceived practical difficulties centred on anticipated problems with enforcement, while philosophical objections focused on the ban’s encroachment into private space and attempt to compel behavioural change. One MP, Claire Perry, represented the choice in terms of a conflict between emotion and reason: ‘Heart says “ban it”, head says “unenforceable bad law”’ (Mason, 2014). While providing succinct insight into one legislator’s mindset, this comment also reproduced a binary distinction between emotion and reason — something that has long informed efforts to ‘banish’ the former from public life (Williams, 2001). Specifically, it presented the protection of children’s health against the risks of exposure to secondhand smoke (SHS) as a principally emotional issue — in contrast to the rational public policy concern for enforceability, a requirement of ‘good law’. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".