Emigrants as territorial international tourism development enablers. The Cilento National Park case
Bibliographic record
Abstract
The work is based on the assumption that Italian southern regions are worldwide known touristic attractions that have been characterized by strong migrating processes in the past century. On these basis, their numerous tourist Small and Medium Enterprises (SMEs) could attempt to take advantage of the strong recalling factor exercised on emigrants in order to strengthen the related “return tourism” flows. In particular we suggest that emigrant themselves could return (at least once in a lifetime) to their ancestors’ land, but mostly could play an important role in the stimulation of their homeland’s positive image in the foreign country in which they moved in, developing the related tourism flow.The above hypothesis is deeply analysed within an ongoing project, commissioned by Foreign Affairs’ Italian Ministry, conducted by Cilento National Park and IRAT- National Research Council. Scope of the project is to investigate on the relational reinforcement between the Cilento SMEs and the foreign countries (United States, Canada, Australia, South America, Argentina, etc.) institutional and private parties, regarding both incoming tourism flows and typical products’ export. The idea that territorial areas may identify specific actions focused on international tourism market stimulation, for their tourism SMEs’ benefit, is hence verified according to systemic strategies, within network theories that highlight aggregating forces toward the system’s territorial centre: the Cilento National Park. In this optic, the Park may assume a guidance role, improving its governance capacity, that may result in the promotion of the territorial socio-economical development, enabling tourist market’s growth as much as the identification of tourist international paths.Evidences show that, in order to accomplish the aim of the project, it seems important to stabilize these relationships, to let them become a network structural component, focused on tourism valorisation of Cilento areas, with a continuous and systematic effort. In other words network has to create synergies not only within local SMEs, but also with far distant actors of the demand, such as emigrants and their communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".