Collaboration gaps and regional tourism networks in rural coastal communities
Bibliographic record
Abstract
Tapping into and creating broad networks is integral to connecting communities and destinations to wider flows of tourists and ensuring local benefits from tourism development. However, little research has probed how communities build these connections. This article examines how tourism stakeholders perceive and practice the work of network-building and assess the challenges they face in pursuing this work in regional tourism development. Drawing on survey and focus group data from Atlantic Canada, we identify “collaboration gaps” between the perceived value of network-building and related social practices. Social practice theory is used to analyse tourism network-building and explain why collaboration gaps exist and persist. Our analysis found three gaps: between meaning and practice; vertical collaboration gaps related to the scale of network-building; and horizontal collaboration gaps related to the range of actors involved in tourism networks. These collaboration gaps can be addressed through a focus on meaning, competencies, and materials as means to foster successful collaborations and overcome gaps.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".