Understanding Nova Scotia's Liquor Control Act: an exploration of employee perception
Bibliographic record
Abstract
This dissertation explores the perceptions of liquor laws and serving practices of industry workers in Halifax, Nova Scotia. The study finds that there is a general lack of understanding, compliance, and regulation of the Liquor Control Act (R.S., c. 260, s. 1.). This paper identifies various factors that are believed to contribute to the over-serving of alcohol, including avoiding undesirable interactions, lack of knowledge of, or belief in, current liquor laws and a desire for profit. The study also explores the serving practices at Nova Scotia wineries and golf courses and provides a careful analysis of the Liquor Control Act (R.S., c. 260, s. 1.). Based on the findings, it is recommended that owners of liquor distribution establishments mandate responsible serving training for all employees to protect themselves from legal repercussions. Additionally, the paper suggests that legislators review the wording of the Liquor Control Act (R.S., c. 260, s. 1.) to ensure more realistic and equitable regulation of the industry. Small sample size and self-reporting bias are some limitations to this research. There are numerous topics that should be considered for further research based on the findings presented in this dissertation. The perception of liquor laws and practices by Liquor Inspectors, the relationship of cart girls and golfers, the drinking culture in Nova Scotia, and the relationship between managers and lower-level employees within the industry are all topics that should be considered for future research.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".