An overview of the state of environmental assessment education at Canadian universities
Bibliographic record
Abstract
Purpose Environmental assessment (EA) is a proactive planning tool designed to consider the ecological, cultural, socio‐political and economic impacts of potential projects, making it a major tool for achieving sustainable development. Meaningful EA requires a bridging of the natural sciences with the social sciences to broaden understanding of the overall environmental impacts of development projects on humans, the natural environment and other organisms. As a result of this complexity, education and training needs are great. The purpose of this paper was to consider EA educational opportunities at Canadian universities and to test a model for the analysis of the state of such education. Design/methodology/approach The research design used a qualitative interactive approach, including a survey of 2001 university course calendars, participant observation and semi‐structured interviews. Findings Results indicate that the number of universities offering EA courses has tripled to 40 since the mid‐1980 s. While this is a positive finding, data illustrate that the bulk of the courses offered are survey‐oriented and introductory in nature, with little opportunity to specialize. This cursory approach is exacerbated by a lack of teaching resources and instructor development. Despite the nature of the courses offered, many professors encourage critical thinking and use innovative teaching techniques, including role‐playing, to promote self‐reflection. In fact, the interdisciplinary approach to the curricula and the promotion of critical thinking outside disciplinary boundaries are two strengths of current EA programming. Originality/value In light of this state of formal EA education in Canada, more research and international level dialogue are warranted.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.027 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".