Bibliographic record
Abstract
Changing demographics at post-secondary institutions in Canada provide opportunities for intercultural learning. Curricular design that engages domestic and international students can result in new ways of knowing, seeing, and understanding multiple perspectives of and connections to place. This paper draws on students’ reflections to highlight the effectiveness of place-based and intercultural pedagogies in cultivating a deeper understanding of multiple perspectives of local landscapes, including those of indigenous, non-indigenous, domestic and international students. This is particularly relevant to the study of tourism, an inherently place- based discipline. L’évolution des caractéristiques démographiques dans les établissements postsecondaires du Canada représente l’occasion d’un apprentissage interculturel. Le design curriculaire qui inclut la participation des étudiants canadiens et étrangers peut susciter de nouvelles façons de connaître, d’observer et de comprendre les perspectives multiples et les relations au lieu. Le présent article puise dans les réflexions des étudiants pour souligner l’efficacité des pédagogies interculturelles axées sur la dimension locale en approfondissant la compréhension des perspectives multiples et des paysages locaux, y compris les paysages des Autochtones, des non-Autochtones et des étudiants canadiens et étrangers. Voilà qui est tout particulièrement pertinent pour l’étude du tourisme, un domaine d’études intrinsèquement lié au lieu.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".