Place-based education and extractive industries: Lessons from post-graduate courses in Canada and Fennoscandia
Bibliographic record
Abstract
Place-based education and, more specifically, field-base education, is increasingly considered an effective way to enhance learning experiences . By connecting academic curriculum with different ways of knowing and practicing, these are seen as potentially transformative experiences that can offer participants enriching opportunities to learn about social realities beyond the classroom . However, critics also argue that place-based learning methods can lack a critical perspective and fail to connect local experience with global phenomena, as well as with theoretical concepts. In light of the challenges and benefits associated with place-based education, this paper examines how this learning approach can be applied to the particular context of resource development studies and how it can contribute to postgraduate training in social sciences more generally. This example adds to a growing body of literature on place-based education, through an analysis of and critical reflection on the pedagogical approach used during two doctoral courses focused on mineral extraction in northern Canadian and Fennoscandian communities. Building on reflections by the course designers and the results of a survey administered to participants exploring the learning outcomes of these two postgraduate field courses, this paper examines how place-based education contributed to or hindered the pedagogical objectives pursued in the courses. It further explores the challenges and benefits of place-based education in postgraduate education and in the field of extractive resources development. After briefly reviewing the literature and defining the concept of place-based education, the paper describes the pedagogical approach used during the two PhD courses. In the third section, the results of the student surveys are discussed along with comments from the organizers, before some thoughts for future postgraduate courses are finally provided.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".