Bibliographic record
Abstract
Understanding the idea of economic integration may be straightforward, but measuring it is not. The academic literature has identified a wide range of measures that capture various aspects of integration. Of these, the four most frequently used measures are product-level prices, factor markets, trade volumes, and product availability. All four are valuable measures that effectively capture different aspects of economic integration. The differences between the measures suggest that some might be more useful in certain contexts than in others. A comparison between the different measures suggests that the last two might generate the most meaningful insights into North American economic integration because conditions in Mexico, a developing country, are quite different than in Canada and the United States. To motivate the different measures of economic integration, the next section of the paper briefly discusses why economic integration is important. As defined above, economic integration is clearly important for growth, which ultimately determines each country's standard of living. Integration also drives change, which often is difficult and is therefore resisted. These changes directly affect producers and consumers, and therefore it is important to be able to identify the results of measures designed to foster economic integration, like trade agreements. The sections that follow therefore discuss each different measure of integration and what they tell us about integration in North America.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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".