Mining projects in transition : intensifying extractivism under the guise of fighting climate change
Bibliographic record
Abstract
""As Quebecers, we could make an important contribution to the fight against climate change by allowing these projects to emerge," says the CEO [of a Quebec mining company]. “What other options are there if we want to one day replace gasoline in our vehicles, boats, and planes? You have a choice between lithium from Australia that was processed in China, or lithium salts from South America, which are very difficult for groundwater, and with working conditions that are perhaps less attractive than in Abitibi.” [Translation] (Léouzon, 2021). This excerpt from a press article is about new projects that aim to contribute to the fight against climate change. More specifically, it is about an open-pit lithium mining project in Abitibi-Témiscamingue that is being challenged for its environmental impact. The line of reasoning expressed above is found in many mining projects involving critical and strategic minerals. How can a natural resource extraction project be legitimized with the argument of fighting climate change? Isn’t there an inherent contradiction in legitimizing projects that have undeniable environmental impacts under the guise of green rhetoric? This article takes a critical look at these forms of justification, focusing on the concept of nature and the relationship between humans and nature. [...]"
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.048 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| 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".