Oportunidad y conveniencia de aplicar la prospectiva al sector turismo
Bibliographic record
Abstract
Desde sus orígenes, la planificación turística ha sentido la necesidad de prever el futuro para limitar la incertidumbre que acompaña sus acciones e inversiones a largo plazo. Sin embargo, históricamente los planificadores turísticos han favorecido el uso de instrumentos cuantitativos frente a los cualitativos a la hora de realizar previsiones de futuro. En el presente artículo se exploran las posibles causas de este comportamiento y se defiende la oportunidad y conveniencia de aplicar los métodos e instrumentos de prospectiva al sector turismo. Con este propósito se define y caracteriza a la prospectiva, exponiendo tanto sus ventajas como inconvenientes. Seguidamente, se presenta una síntesis de los resultados obtenidos en una serie de ejercicios realizados recientemente en el sector turístico español, en los que se observa el valor añadido que pueden llegar a proporcionar los instrumentos prospectivos al proceso planificador. Finalmente, se ponen en evidencia las barreras que dificultan la difusión de la prospectiva entre los planificadores turísticos y se recomiendan una serie de actuaciones para incorporar de forma efectiva la prospectiva en el ámbito turístico.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".