Subsidized Regional Airlines as a Sustainable Development Mechanism for Remote Locations Heavily Dependent on Air Transportation
Bibliographic record
Abstract
The understanding that air transport induces the economic development of a region has motivated studies on regional air transport and the necessary subsidies for its implementation. Several countries have implemented specific subsidy plans with a focus on integrating their territories through a network of air routes, depending on the funding methods and the results of the investments that were made. However, few studies summarize the geographical conditions of the locations served that justify their dependence on air transport as the only viable means of accessibility. This article seeks to identify the geographical characteristics of these locations to illustrate the conditions that justify the need for subsidies for the operationalization of air transport as a mechanism to promote the minimum sustainability conditions for such locations. We collected socioeconomic data of a set of 1365 subsidized routes of 28 countries from the Americas, Asia, Europe, and Oceania and tracing a profile of localities, classified in nine different clusters. The results indicated that isolated locations in islands, in the polar regions and areas of impenetrable forests have an almost exclusive dependence on air transport as a means of access and that justify the maintenance of subsidies to air routes as a fundamental requirement for the accessibility of the population to services of a social and humanitarian nature.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".