Competing Notions of Diversity in Archipelago Tourism: Transport Logistics, Official Rhetoric and Inter-Island Rivalry in the Azores
Bibliographic record
Abstract
Contending and competing geographies are often implicitly involved in archipelagic spaces. Various small island states and territories with multi-island geographies have flourishing tourism industries that presuppose an archipelagic experience: visitors are encouraged to explore and sample different island constituents of the territory. This strategy taps into different tourism niche markets, improves local value added, and shares tourism revenue beyond key nodes and urban centers. The organization of such an important economic activity however often reflects a ‘one-size-fits-all’, tightly coordinated, frequently contrived process that does not necessarily speak to the cultural and biogeographical forms of diversity that reside in the archipelago. This paper offers the notion of archipelago as a new way of rethinking problems and challenges encountered in island tourism, and then assesses the implications of this conceptualization on the representation of ‘the archipelago’ in the Azores, Portugal, and reviews what this approach means and implies for sustainable tourism policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".