Bibliographic record
Abstract
The development of climate change action plans and strategies is usually done via the policy cycle during the first half of a government’s term. This short-term political process is at odds with the longer-term climate change issue that requires a consistent and sustained effort. Consequently, this often leads to conflicting and ever changing climate plans and strategies that often do not fully move to implementation. Several key strategic questions need to be considered at the policy agenda setting stage. Examples of these questions include: the real impetus for developing the plan, political will to take on policy development at a particular time, the degree of intention to actually implement it, and depth of target vs. costs to the economy. The developmental stage of climate plans in Canada has historically involved five key components (with many variations): 1) background policy and scientific work; 2) consultation process; 3) economic/policy analysis and target setting; 4) building political support for a greenhouse gas target and policy package to meet the target; and 5) refinement and final political approval. Businesses are also responding by developing climate change strategies to either hedge their risk of being regulated, hedge their risk related to severe weather events, and/or to take advantage of climate business opportunities.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.084 | 0.028 |
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".