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Record W7079449772 · doi:10.26108/tfc1-a674

Understanding Nova Scotia's Liquor Control Act: an exploration of employee perception

2023· article· en· W7079449772 on OpenAlexaboutno aff

Bibliographic record

VenueAcadiaU-DEV · 2023
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsNova scotiaMandateControl (management)PerceptionSample (material)Qualitative research

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.678
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.156
GPT teacher head0.300
Teacher spread0.144 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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