Bibliographic record
Abstract
This paper narrates the experience of a Canadian university in reorienting its mining curriculum towards the goal of producing engineers who are sensitive to context. The authors acknowledge industry’s historical association with environmental degradation and imperialism, but counter that mining is necessary to provide the materials of civil infrastructure, particularly that required to reduce green-house gas emissions. They also point to the potential for mining projects to rejuvenate communities, by providing the revenues that support self-determination, suggesting that more equitable distribution of impacts and benefits may be achieved through engineering design that is better informed and sensitive to community perspectives.To promote such an approach to engineering design, the Robert M. Buchan Department of Mining at Queen’s University has instituted a program of curriculum re-positioning that is informed by theories of situated and transformative learning. This paper traces the first steps in the development and execution of curriculum that supports the development of student awareness of context and culture and a new contextually-sensitive approach to professional practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.029 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".