Research in a culturally diverse world: reducing redundancies, increasing relevance
Bibliographic record
Abstract
Purpose This paper aims to identify means and ways to reduce redundancies and increase relevance in tourism research in a culturally diverse and globalised world. Design/methodology/approach The content of this paper is based on minutes of an extensive discussion (panel as well as townhall-type of discussion) at the 2015 AIEST conference in Lijiang, PR China. Findings Challenges in today’s tourism research world are identified and ways of how to deal with them are shown. Some of those solutions might provoke change in certain domains. This is why ideas are provided for the AIEST to support and facilitate this change. Researchlimitations/implications Limitations come from the research settings of this contribution, which is essentially based on records of a panel and a townhall-type discussion. Originality/value We try to provide food for thought, in order to provoke one or the other discussion. This is why we are happy to receive feeback.
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 | 0.309 | 0.398 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.024 | 0.020 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.006 | 0.006 |
| 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".