A quarter-century of contributions to tourism scholarship: tracing historical themes and citation patterns in current issues in tourism
Bibliographic record
Abstract
This research delves into the intellectual underpinnings and citation performance of Current Issues in Tourism (CIT) over the past 25 years. It uses bibliometric methodology, content analysis, negative binomial regression, and quantile regression to answer its five research questions. This multi-pronged research revealed significant contributions made by the CIT to the tourism domain. We present three key contributions: delineating the journal’s major themes, analyzing thematic evolution over time, and understanding citation drivers. By identifying the dominant themes within the journal, this study provides valuable insight for prospective authors. It also explores thematic evolution within the journal, which sheds light on emerging and declining areas of interest. This knowledge can be invaluable for researchers seeking to position their work at the forefront of the discipline. The negative binomial and quantile regression analysis offer further contributions, identifying and evaluating variables associated with an article’s citation count. This information provides valuable guidance for aspiring researchers seeking to maximise the reach and impact of their publications.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.022 | 0.040 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".