Bibliographic record
Abstract
As we write this, climate change is afecting Canadians with frightening regularity.Heat domes across western Canada cause sudden deaths and leave forests tinder dry.Hurricanes have devastated many communities on the east coast.Fires have destroyed communities and transformed ecosystems across the country.Te twin threats of fooding and drought disrupt what we used to consider normal.Te impacts and risks of climate change will continue to worsen until we decarbonize our energy systems and get to net-zero greenhouse gas emissions globally.Our fossil-intensive economy needs to change, rapidly.Te COVID-19 pandemic has shown that we can manage disruption and change, yet even as the technological miracle of vaccines mitigates the worst of the pandemic, we're seeing how its impacts are exacerbated by social and racial inequities.Te refrain that "we're all in this together" ultimately rings hollow when we acknowledge the unequal burdens borne by marginalized people and communities.Climate change and social injustice are forcing a reckoning in how we produce, transport, and use energy.Like viruses, energy systems are the sort of thing that most people don't pay attention to until there's a crisis.To all of this has now been added the war in Ukraine, which has thrown the relationship between energy systems and international security back into perhaps their most stark relief in the post-Second World War era.Te impacts of the war on energy policies, at least in the near term, are growing
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.476 | 0.342 |
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".