Bibliographic record
Abstract
An ongoing concern with many Canada’s governments is avoiding climate change related policy failure, including that associated with climate change. In response, there has been a spate of government-led climate change vulnerability and risk assessments, studies, and strategies. With a growing attention on developing the ‘right’ policies and program to address climate change needs to be examined as an important factor in ‘adaptive capacity’. As governments turn their attention from broad strategizing to policy-making, we argue that a consideration of the often overlooked micro-level and seemingly routine government based capacity—especially the advice needed to formulate and implement policy changes—is required. A high level of policy capacity is an important factor in avoiding policy failures. The questionnaire was delivered through a webbased survey of 1469 Canadian provincial and territorial government policy analysts working in nine provinces and three territorial jurisdictions in the climate change, environmental, financial, forestry, natural resource, infrastructure, transportation, and water sectors. A comparison of mean scores across key indicators of policy work was conducted. A number of policy implications were raised. First, those in financial sector do very little climate change policy work. Second, the fracturing of roles in those departments responsible for forestry reflects the complexity of the climate change issue and a developed division of labour. Those who identified with forestry sector, under performed despite their concern about climate change, in terms of key policy tasks, the level of complexity that the issues were addressed and a low level engagement with stakeholders with those outside of government. Policy capacity was also undermined with a view that departments were committed vis a vis their mission statements but that this commitment was not reflected in their daily operations.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.047 | 0.015 |
| Scholarly communication | 0.018 | 0.003 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".