Bibliographic record
Abstract
This chapter presents a new measure of competitiveness called “market mass” and uses it to examine the competitive position of forty Canadian food and beverage manufacturing subsectors relative to their United States counterparts. Competitiveness is a comparative concept. A firm or industry or country is measured as being competitive relative to another such entity. A possible response to the evident unimportance of Canada-US trade in this sector is that the Canadian food economy is autarkic. Due to tariffs, transport, or other trade costs, Canadian firms are simply not subject either to pressures from import competition or opportunities to export. Profitability must be adequate to sustain the activities of the firm, subsector, or industry, and “competitiveness” must not be “bought” by means of artificially low profitability. In fact, four-firm seller concentration ratios are higher in Canada in all but two of the forty subsectors, reflecting the much smaller market size in this country.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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; both teacher heads agree on what is shown here.
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".