Borders within. Tourism growth, migration and regional polarization in Baja California Sur (Mexico)
Bibliographic record
Abstract
Tourism in Baja California Sur (BCS, in the Baja California Peninsula in Northern Mexico) can be understood as an example of the internationalization of the services sector and the integration of relatively isolated regions to the processes of the global market, particularly that of the United States. The success of the Los Cabos corridor (between San Jose del Cabo and Cabo San Lucas in the southernmost tip of BCS), as one of the most dynamic tourist spaces in Mexico, has been central to this trend in the state. Subscribing to a flexible understanding of borders (Brunnet‐Jailly 2005) that goes beyond geographical boundaries, this growth has implied a restructuring of intra and interregional interaction patterns not just in Los Cabos but in the whole state. International tourism has fostered internal borders in BCS, due to the enclave nature of tourism growth as expressed by increasing investment concentration, income inequality, and social exclusion. This article analyzes the effects and perspectives of a tourism growth that is tightly related to the US market, has changed demographic patterns locally, and has increased opportunities for BCS, even though the growth has also made BCS more vulnerable to negative external impacts, including environmental deterioration, intraregional economic disparities and social exclusion. The text is divided into three sections: the first addresses the economic importance of tourism internationally and for Mexico; the second part highlights the most recent patterns in the sector's performance in BCS, to which Los Cabos as the state's most important tourist destination is crucial; and the third reflects upon the relationship between growth and development as they are linked to tourism in the region. A call is made about the need to reconsider how to sustain growth while changing the development of negative tourism‐related patterns, a concern that government officials, private investors and the local community should share and act upon.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".