Decoupling climate-policy objectives and mechanisms to reduce fragmentation
Bibliographic record
Abstract
The efficacy of climate-change mitigation policy within the building sector is examined in terms of how fragmentation can limit the extent of mitigation actions that can be achieved in a timely manner. The policy and regulatory context for the building industry is examined in relation to the policy context for solutions and recommendations that will work for all parties. Based on this analysis, two substantive recommendations are made for improved policy design. Firstly, a decoupling of policy objectives and policy mechanisms is needed so that the policy-taking stakeholders (in design, development and construction) can reduce energy use in buildings more effectively. Secondly, policy-taking stakeholders need an explicit and diverse system in order to advocate for policy objectives. The major aspect of this work is the development of a new conceptual framework that ties together these recommendations into a continuous process of policy-making and policy-taking. This framework demonstrates an idealized system that operates simultaneously top down and bottom up, and the development of policy objectives is influenced by stakeholders of all kinds to further the goals of an energy-efficient, low-carbon built environment.
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 | 0.031 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".