Students’ relationships to knowledges, place identity and agency concerning the St. Lawrence river
Bibliographic record
Abstract
Our study is framed according to a transformational-sociocritical approach to an education for sustainable development (ESD) that, in particular, considers landscapes as learning contexts of potential use for fostering engagement in the areas of environmental protection and sustainable development. In keeping with this perspective, we surveyed students aged 16–17 years from the Lower St. Lawrence region (Quebec, Canada) concerning their knowledge and perceptions of issues pertaining to the St. Lawrence, the river facing them on an everyday basis, and about their role as citizens in that connection. In respect of theoretical considerations, we propose developing the concepts of relationships to knowledge, place identity and agency. An analysis of questionnaires (N=334) has served to: bring out the learnings and issues pertaining to the St. Lawrence River that students consider to be meaningful; characterise the various identity-centred relationships with this landscape; and define the eco-civic agency of the students surveyed. The results of this analysis then provide a basis for some proposals for an ESD curriculum relating to the St. Lawrence River that is rooted in students’ concerns. While the environmental issues dealt with occur locally, the potential transfer of results into other curricula and educational contexts is also discussed.
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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 | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".