The Role of Academia in Assessing an Environmental Disaster
Bibliographic record
Abstract
The University of Northern British Columbia (UNBC) is a small, research-intensive university with a main campus in Prince George. It has several smaller campuses (e.g., Quesnel) as well as two research forests (John Prince and Aleza Lake) and a research centre (Quesnel River Research Centre, QRRC; www2.unbc.ca/quesnel-river-research-centre) that focuses on aquatic sciences, mainly within the Quesnel watershed. The catastrophic breach of the tailings storage facility (TSF) of the Mount Polley Mine in 2014 occurred about twelve kilometres from the QRRC and affected large parts of the Quesnel watershed. Given this close proximity, and past experience in the watershed, researchers from UNBC, along with other institutions, were uniquely positioned to study the aquatic impacts of this event. To date, the UNBC-led research is probably the largest academic study on the spill. This research has provided important information on the impacts on the downstream, receiving aquatic environment. In addition to peer-reviewed journal publications, presentations to provincial and federal agencies, both in Canada and abroad, and presentations at national and international conferences, the research team has also disseminated the findings through other outlets such as community meetings, an annual research open house hosted by the QRRC, media interviews and documentary films. This research has been generally well-received as being trustworthy and independent. However, the research team has recognized that there are aspects that have not met the expectations of all local residents and other concerned individuals in the region. This includes the expectation of immediate response to individuals’ concerns and of the fast turn-around of results and reporting. In part, this stems from the training that academics ‒ both graduate students and professors ‒ receive and the venues in which they are accustomed to reporting their results. It also stems from the diverse range of perceptions of the event on the environment ‒ from “it’s not a big deal” and “it was just like a natural landslide” to “it’s an environmental catastrophe” and “I will never eat the fish” ‒ and the misperception that academics operate on the same timeline as consultants in regards to the timing and distribution of academic research findings. Here we describe aspects of the spill, including the initial impacts on the local environment, and summarize some of the main research findings based on the first ten years of study. We also provide a personal reflection on some of the problems encountered and provide some thoughts on what we have learned along the way, explaining how academics undertake research on disasters like Mount Polley. We start with a discussion of why environmental disasters associated with human activities, such as mining, need to be considered as different to natural ones, and how this can shape the nature of research.
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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 | 0.054 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.029 | 0.010 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".