Support Place-Based and Inclusive Supply Chain, Employment and Skills Strategies for Housing-Energy Retrofit
Bibliographic record
Abstract
Policy Highlights To achieve the recommendation stated in the chapter title, we propose the following: Member States should empower municipalities with resources and training to develop Building Renovation Plans supported by One-Stop Shops focused on inclusive local supply chain development, employment and skills priorities, as well as serving housing retrofit consumers. Municipalities should use procurement frameworks and Direct Labour Organisations to ensure a pipeline of retrofit work and support training for good quality employment. Member States should implement licensing or minimum competency standards for housing retrofit professionals, ensuring certification schemes encompass a wider range of skills. Retrofit is an opportunity to enable new groups to enter the construction sector. Municipalities should co-create partnerships alongside employees, and support unionisation, to promote training and work opportunities for women and minorities. Developing inclusive pathways to a skilled housing-energy retrofit workforce is a socio-technical problem, requiring insights from social, policy, building and engineering disciplines, because retrofit interweaves human and technical practices and processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.010 |
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".