ACW Baseline Report: Built Environment
Bibliographic record
Abstract
This paper was presented at the Adapting Canadian Work and Workplaces (ACW) International Workshop in Toronto, Canada, November 2015. The goals of the paper are: 1. To establish the current state of knowledge about the contribution of the workforce to ‘greening’ the construction industry; 2. To assess the potential of labour to shape the industry’s carbon footprint. 3. To identify barriers to the successful participation of the workforce in developing pathways to low carbon construction and develop strategies to circumvent these barriers. 4. To identify needed modifications to employment, employment conditions, working practices and the overall organization of construction work that will improve the capacity of the workforce to implement low carbon construction (effective health and safety provisions, integrated team‐based work practices, improved vocational education and training (VET), union representation and a greater say for the workforce in shaping the industry’s future). 5. To examine the current and potential role of unions and professional organizations in advancing this process. 6. To analyze the workforce implications of widely used policy tools, such as energy efficiency targets, building codes and contract procurement requirements in facilitating the transition to low carbon construction. 7. To carry out research on the role of workers and the organizations that represent them in implementing specific, innovative low carbon projects which can serve as models for wider application in the building industry.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 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".