Bibliographic record
Abstract
The rapid economic, social and technological changes condemn organizations to obsolescence. To keep up the pace, several factors associate with innovation were identified, including the establishment of local area networks and the implement of knowledge transfer program. . The creation of social capital, between people and between companies, of the same geographic area or industrial sector can also stimulate innovation. Touristic organizations are not different from other economic sectors. They need to create and exchange information to ensure their adaptation to the new conditions and improve their ability to deal and resolve problems. However, some of their characteristics are unfavourable (i.e. size, localization, labour force). Moreover, research and development in tourism have often been seen a spiritual supplement for leaders and political officials in tourism. By taking the example of the Chair in partnership on innovation and attractiveness (Quebec-Charlevoix), we invite you to share reflections on how to face challenges regarding creation and knowledge transfer in order to increase touristic attractiveness and foster innovation within tourism businesses.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".