An Examination of Perceived Pub Culture at Three Downtown Toronto Drinking Establishments
Bibliographic record
Abstract
Drinking (alcohol) has been central to most human cultures. It is traditionally a social activity associated with celebration in public locations and is a culture unto itself. ‘Pub culture’ has evolved and can be examined by seeing how pubs assert their identity and culture on social media. Unfortunately, most research on drinking has taken a ‘problem-oriented’ approach, so there is a need to study it as a complex sociocultural phenomenon. This can lead to better understandings of the cultural factors associated with problematic drinking. This paper investigated the pub cultures of three downtown Toronto pubs through their Instagram accounts. Examining variables like pub tradition, drink types, inclusion, ‘being local,’ and references to sports, music, and entertainment, this paper sought to augment our understanding of pubs as places of community and social interaction. It found three sub-pub cultures—1) live music, 2) high-end food and elegance, and 3) sports. None of them appeared to overtly promote alcohol. If anything, alcohol was merely a convenient staple framed around the businesses’ primary space utilisation. Each pub exhibited a unique online identity that was characterised by elements of human interaction and sociability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".