Why is research–practice collaboration so challenging to achieve? A creative tourism experiment
Bibliographic record
Abstract
Within tourism research, there has been little attention to research–practice knowledge exchange during the research process nor to practice-based research. This article examines a research-and-application project on creative tourism in which research–practice collaboration is explicitly foregrounded and made central. Through a reflexive process, the challenges this hybrid approach embodies and the pragmatic dilemmas that accompany the complexities of building closer research–practice relations and capturing practice-based knowledge are examined in three strategic areas: developing spaces for ongoing knowledge exchange, enabling practitioners to take on the role of co-researcher, and fostering researchers’ close attention to the application side of the project. In the context of the CREATOUR project, hybrid roles question who can do research, reinforce consideration of the added value of research processes for practitioners, and lead researchers to go beyond traditional research activities, with this ‘disruptive’ context causing tensions, uncertainties, and dynamic co-learning situations. Ongoing interactions over time are necessary to build relations, understanding, and trust, while flexibility and responsiveness are vital to address emerging issues. Training on research–practice collaboration, knowledge transfer, and mentorship techniques for both researchers and practitioners is advised. Challenges in integrating practice-based knowledge directly into research articles suggest a customized communication platform may be a useful ‘bridging’ mechanism between practice-based and academic knowledge systems.
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 | 0.136 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".