Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it