Climate literacy in the political forum using Canada as a case study
Bibliographic record
Abstract
Evidence-based policy is still lacking in decision-making in Canada and around the world. As much of the world now faces concurrent crises among which are climate change, a global pandemic, and rising wealth inequality, the relationship between politicians and scientists is more important than ever. Climate literacy among office-holders, public servants, and regulators is critical for ushering in change and much needed transformation. Using Canada as a case study, this presentation from an engineer-turned-politician will discuss (1) the progress that has been made in climate literacy, with particular attention to its evolution in the political forum and the role of politicians, (2) a discussion of Canada’s national and the global response to climate change and its link with the COVID-19 pandemic, (3) the climate literacy in the public sphere and the obstacles to its health including undue corporate influence and disinformation. Politicians have an ethical duty to uphold the interests of their constituents, a duty that extends to the environment and to future generations; best-available science rather than the allures of crony-capitalism must win out the tug-of-war to realize that duty.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.035 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".