Bibliographic record
Abstract
By deconstructing Canada’s current system of environmental management, improvements to current policies can be made in order to reach goals which not only meet but exceed environmental expectations provided by the United Nations Framework Convention on Climate Change (UNFCCC). Canada’s Changing Climate Report (CCCR) 2019 has shown the highest rates of warming ranging from 1.7 °C to 2.3 °C since their initial report in 1948. This report from 2019 indicates that Canada is experiencing global warming at twice the rate of the 0.8 °C global average (Bush and Lemmen). Canada’s nationwide audits have stated goals to cut emissions, but no detailed plans, timelines, funding or expected results have been provided. No level of government in Canada is prepared to apply these actions nor adapt to the impacts of climate change. With several targets and no initiative to reach them, it is clear that Canada’s system of environmental management needs improvement. Reconstruction of communicative practices and reformation of current policies can ensure sustainability across all sectors. Through communicative changes and policy reforms, effective changes to both the economy and ecology can be reached. Deliberative polling in a networked environment lays the foundation toward representing non-human actors (non-human nature and future generations), full transparency, and sustainable goals.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.050 | 0.011 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".