Sustainability Tours: A case for Tours as an Essential Component of Educating for Sustainability
Bibliographic record
Abstract
This research paper is based on a broader exploratory case study of a sustainability tour that the authors undertook. The larger study explored “the concept of learning application through the case of a sustainability tour” (Gillard, 2016, p. 226 ). The tour was part of a certificate program for sustainable community development offered to mid-career professionals through a continuing education unit at a large Canadian academic institution. Employing qualitative methods, the authors conducted semi-structured interviews, then analyzed the data, including course documents, to garner participants’ perceptions of what they learned “on tour” as well as how their learning had subsequently been applied. The study also identified salient features of the tour and the ways in which the tour format (or other contextual factors) may have inhibited learning and its subsequent application. This research paper presents some relevant findings, practical implications, and lessons learned regarding the sustainability tour pedagogy.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".