Narratives of a “Dental Oasis”: Examining Media Portrayals of Dental Tourism in the Border Town of Los Algodones, Mexico
Bibliographic record
Abstract
The dental tourism industry situated along the northern Mexican border provides care primarily to American and Canadian tourists crossing the border to access dental treatments that cost less than domestically provided. This movement of patients across the Mexico–United States (US) border supports the practices of numerous dental clinics in northern Mexican border towns. The largest concentration of dentists per square kilometer in this region is situated in Los Algodones, Baja California. Media articles published in American and Canadian newspapers have described the services provided by the roughly 500 dentists working in this small border town. This paper outlines the overall narrative presented in media articles published in common dental tourists’ homes to identify how this industry site is portrayed to industry stakeholders. We argue in this paper that the common narrative presented by the media suggests that this particular industry site is necessarily improving access to dental care and economic development without discussing in detail for whom these health and economic benefits are provided and under what conditions or structures of control. We raise concerns regarding this overly simplified and unbalanced media portrayal of the industry as it fails to consider the perspectives of industry stakeholders on both sides of the Mexico–US border. In particular, this paper draws attention to the missing perspectives of individuals with continued poor access to dental care and/or economic resources despite involvement in dental tourism activities in industry sites like Los Algodones.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".