Immigration and <scp>C</scp>hinese food preferences in the <scp>G</scp>reater <scp>T</scp>oronto <scp>A</scp>rea
Bibliographic record
Abstract
Abstract This paper presents important socio‐economic characteristics of C hinese C anadians in the G reater T oronto A rea ( GTA ) and the impact of these characteristics and acculturation on their expenditure on and consumption of ethnic vegetables. These consumers purchase ethnic vegetables based on attributes such as quality, traceable production, versatility and language. The factors that predict expenditure on ethnic vegetables among C hinese C anadians are as follows: the percentage spent on food out of total monthly income, years spent in C anada and acculturation. The estimated demand per month for C hinese ethnic vegetables in the GTA is $ CAD 21 million. Meeting this demand with ever more locally produced vegetables will reduce ‘food miles’, enhance niche market production and facilitate greater agricultural sustainability in O ntario.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".