The Role of Air Transport Infrastructure Towards Breaking Seasonality in Emerging Tourism Destinations: A Qualitative Study of Akureyri, North Iceland
Bibliographic record
Abstract
This paper examines the impact of air infrastructure development on emerging insular destinations by focusing on Akureyri, located in North Iceland. It examines the intricate relationship between airport infrastructure capacity expansion, the building of human capacity, hospitality infrastructure growth, and seasonality challenges. Through extensive qualitative interviews and literature reviews from stakeholders, policymakers, and academics, the research synthesises how an airport expansion offers democratisation of tourism mobilities and connectedness with the outside world for remotely located communities in the region. The analysis found that despite the stakeholders’ increased awareness of the complexities in accommodating year-round tourism, there is a readiness to increase the tourism enterprise flows. Nevertheless, the study shows a high deficit in hospitality infrastructure along with regional investment imbalances, revealing the lack of a comprehensive capacity building strategy. Ultimately, the authors advocate for an ‘extended capacity’ approach to tackle the challenges presented in the process of overcoming seasonality issues. The paper stresses the need for developed human skill capacity and diversified services and products alongside infrastructure capacity upgrades. The gained insights are gathered in two figures. The first identifies and maps the contextual landscape encompassing North Iceland’s tourism industry and its stakeholders, while the other showcases the complexity of the process the destination undergoes for breaking seasonality, while exposing the interconnections of potential outcomes, stakeholders’ needs, and the existing and in process capacities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".