Differing approaches to embedding low energy construction and climate literacy into vocational education and training
Bibliographic record
Abstract
This chapter presents varied approaches across Europe and North America to embedding climate and energy literacy into construction occupations through programmes of vocational education and training (VET). These approaches involve different coalitions of stakeholders and range from those in which the public sector and the unions play a key role to those largely reliant on private sector, employer-driven initiatives. VET for low energy construction (LEC) may be mainstreamed into comprehensive, long-term programmes for all construction occupations or consist of short courses imparting the specific skills required to carry out individual tasks. The chapter draws on a project, Building it Green, seeking to embed climate literacy into the building trades and identify good practice examples in the coordinated market economies (CMEs) of Belgium, Germany and Sweden and the liberal market economies (LMEs) of Canada, the United States (US), Ireland and the United Kingdom (UK). Each case is examined in relation to the involvement of different stakeholders, the VET model and the approach taken towards including labour, whether Taylorist or aiming to empower. The chapter reveals sharp differences in the importance attached to VET for LEC in Europe and North America. Examples of good practice are found in the comprehensive VET programmes of Belgium and Germany, the state-supported VET for LEC centres in Ireland and a UK local authority direct labour organisation. While the unions have a significant role in Canadian VET, climate literacy is only recently emerging as a significant focus. The United States has positive examples, particularly at state level, but is hampered by low union density and, as also in the United Kingdom, lack of consistent government policy on climate mitigation. The chapter concludes that equity and valuing labour are key to combatting climate change.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".