Accessible Tourism: A Bibliometric Analysis from 2000 to 2021
Bibliographic record
Abstract
The term "Accessible Tourism" was first used in 1989.This term has the characteristic of being transversal.The aim of this work is to find out the current state of scientific production in this area of tourism in order to be able to answer future questions and identify new lines of research.To this end, we have carried out a bibliometric analysis covering the first two decades of this century.After defining the keywords, a search was carried out in Web of Science and the results were analysed quantitatively and qualitatively using VOSviewer.This analysis shows that from 2018 onwards, publications increased considerably, reaching a peak in 2020, mostly in the areas of geography and economics, as research in the area of tourism is still very recent.The number of authors is reduced to around twenty, with one of them standing out significantly in terms of number of publications and citations.The conceptual analysis shows the variety of terms used by researchers and how they have evolved over time, the most commonly used being "people" and "disability", with others appearing such as "inclusive tourism" connected to "management" or "sustainable tourism", which are more akin to current management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.138 | 0.200 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".