CENTRO ACADÊMICO DE EXCELÊNCIA EM LAZER DA VANCOUVER ISLAND UNIVERSITY, CANADÁ
Bibliographic record
Abstract
The World Leisure Center of Excellence in Sustainable Leisure Management brings together established and emerging scholars from around the globe to share innovative ideas, engage in dialogue and collaborate in research and teaching (WORLD LEISURE CENTRE OF EXCELLENCE, 2012). Bridging the World Leisure Organization (WORLD LEISURE ORGANIZATION, 2016) and the host campus of Vancouver Island University, the Centre emerges as the leading source for information, research (http://www.viu.ca/slm/WorldLeisureCentreofExcellence.asp) and collaboration surrounding sustainable and innovative leisure best practices. Through shared learning experiences on regional, national, and international levels, the Centre will foster inquiry and distribute knowledge with an established network of community, scholars and professionals. This article will present the path of the Center, as well as background and context to provide a better understanding of the WLCE's objectives and commitments for the future. It also includes examples of the events and activities developed by the Center that show its ongoing effort to continue working in knowledge exchange, fostering sustainable practices within the leisure context and future projects.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.142 | 0.022 |
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".