An analysis of the literature at the nexus of heritage, tourism, and local economic development
Bibliographic record
Abstract
It is clear that tourism is an important industry. It is equally true that it is being pursued in many areas as an alternative to more traditional economies that have failed. Often tourism is based on local heritage resources such as older buildings and customs that attract visitors. The concepts of heritage, tourism, and local economic development are therefore linked in many places but researchers have not necessarily explored all the possible links. This paper first selects articles and books that deal with all three of these subjects, draws out the themes contained in them, and finally analyzes the relationships among them. The works identified were classified according to their focus, what they are about; their locus, where they refer to; and intent, what the authors set out to do. Each one of those categories was then subdivided further. The extent to which papers in these different categories relate to each other or overlap was described. The purpose is to expose the places where more work is needed and where research opportunities lie. While the strongest link is between papers that are analytical in intent and deal with rural tourism, there are also connections between papers analytical in intent and focussed on society or economic development. Weaker connections are noted between studies that are theoretical in intent while focussing on and tourism or society.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.033 | 0.045 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".