CSR as a strategy for public-private relationships in protected island territories: Fuerteventura, Canary Islands
Bibliographic record
Abstract
This article reflects on governance and Corporate Social Responsibility (CSR) as a competitive strategy. It shows that synergies for achieving sustainable tourism destinations require innovation, inter-business cooperation, and public-private cooperation. The empirical analysis focuses on the island of Fuerteventura, Canary Islands. The island is an outermost territory of the European Union, where the high number of tourists has an important socio-environmental impact. Fuerteventura has also been designated a Biosphere Reserve due to respect for its cultural, natural and scenic values and the manner in which commitment to renewable energy, responsible water management and responsible fishing have contributed to its sustainable development. This recognition has led to a coordinated decision-making process, which has resulted in the implementation of different plans to modernize this tourist destination. In fact, the island has been divided into three basic zones that differ in the conservation levels pursued and the activities allowed in each of them. In this context, this article aims to analyze the influence of innovation, private-public collaboration and private-private collaboration on tourist accommodation companies regarding their level of integration of CSR in an island designated as a Biosphere Reserve
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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 | 0.002 | 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 | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".