Insights into Canadian consumer perceptions and behavior in the lobster industry: Implications for sustainability and economic development
Bibliographic record
Abstract
Previous research within the lobster industry has often overlooked significant determinants that influence perceptions of lobster consumption. In response, this study aims to examine Canadian perceptions toward the Canadian lobster industry and to assess the impact of socio-demographics, consumer motivation, sustainability, economic diversity and equity, and traceability on the enjoyment of lobster consumption. An extensive cross-national survey was conducted, following a conceptual framework. Descriptive analysis and cumulative link models were deployed for data analysis. The outcomes indicate that Model 5 exhibits the best fit. Based on the findings from Models 2 and 5, key determinants influencing consumer preferences for enjoying lobster consumption include a preference for vegan diets, marital status, one's history of lobster purchases, the frequency of lobster consumption, the perceived importance of price, and support for the Canadian economy. In future research, natural language processing (NLP) techniques will be employed to gain deeper insights into consumer perceptions and behaviors within the Canadian lobster industry.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.006 | 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".