Teaching about spatial navigation: some (inter)disciplinary insights
Bibliographic record
Abstract
For researchers interested in Spatial Information Theory (SIT), sharing concepts, methods, and tools is a fundamental requirement for the advancement of interdisciplinary collaboration. However, topics related to SIT are still predominantly taught on a disciplinary basis at universities. In this exploratory study, we searched for the elements that are commonly shared among disciplines when teaching about a SIT-related topic such as spatial navigation. Non-directive interviews were conducted with five professors and lecturers from various academic fields at Université Laval (Quebec City, Canada). The goals, concepts, methods, and tools employed to teach about spatial navigation were analyzed using a reflexive thematic analysis framework. Highlights of the study concern the purpose of working on spatial navigation, the conceptual framework the students should know, and the pedagogical approach that is used. This exploratory study reveals that despite their diverse backgrounds, teaching professionals working on spatial navigation have much in common. These results open perspectives for developing teaching materials that could be shared across disciplines to train the future generation of SIT researchers from an interdisciplinary perspective.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".