Bibliographic record
Abstract
We would like to thank Waterman and Stolp for their interest in fostering the discussion on US–Mexico border health issues, and the Journal for allowing us to clarify one of the most important concepts developed in our December 2003 article. As we mentioned (p2017, first paragraph), and as Waterman and Stolp say in their letter, there was nothing purposely included in NAFTA to enhance collaboration on health-related issues with counterparts on the other side of the border. But this issue is beside the point. The argument we make is that NAFTA has not facilitated the erasing of the constraints that impede collaboration between health workers. We show that in a few instances it has increased them. Our conclusion is that globalization, as exemplified by NAFTA, benefits not the health of the people, but that of the transnational corporations. Looking at the impact of NAFTA in Canada, Labonte has arrived at similar conclusions.1 The contribution of our fieldwork is to detail how this occurs at the US–Mexico border. Other free trade agreements—the Free Trade Area of the Americas Agreement, the US–Central American Free Trade Agreement, the US–Australia Free Trade Agreement, the pending US–Morocco Free Trade Agreement, and others—do include health-related clauses, and the overwhelming assessment by experienced health organizations and observers in the field is that if Congress approves these agreements the damage to the health of these countries will be great.2–8 The need for binational collaboration is well recognized, and several agencies have invested considerable resources with different levels of success. Witnesses to these efforts agree on the slowness of these processes. A good example is the US–Mexico Border Health Commission: the idea was conceived in 1990, the creation of such a commission was approved by the US government in 1994, the commission was established in July 2000, its first official meeting was held in November of the same year, and the bylaws were approved in February 2003. As we explained in our article, executives of binational agencies need to take a deeper look at the context in which they are operating to be successful. If contextual constraints to their success are not systematically addressed, progress in US–Mexico border health will continue to be slow, marred with difficulties, and expensive in terms of both human and economic resources.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.020 | 0.023 |
| Insufficient payload (model declined to judge) | 0.067 | 0.021 |
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".