Bibliographic record
Abstract
There is a broad consensus among climate scientists and policy makers that reducing the GHG emissions and energy consumption of the built environment is critical to mitigating the impact of global warming. This means adopting policiesthat will dramatically accelerate the introduction of low carbon construction, both in new structures and in retrofitting the existing building stock. An important, but often overlooked, component of this process is how to ensure that climate objectives are properly implemented at the workplace by building trades workers. This paper argues that for this to happen, there must be major changes in how the industryis organized – changes that challenge the unregulated market with its pervasive reliance on labour sub-contracting and precarious employment and its negative impact on building standards. The industry must transform its inadequate training and apprenticeship system to give workers the additional skills needed for low carbon construction. It must reduce the loss of qualified workers and their skills, by providing greater job security and a long term career in the industry. And it must incorporate a greater role for the workforce - and the unions that represent it - in creating a ‘green’ construction culture at the workplace.
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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.042 | 0.005 |
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".