Towards a Standardized Framework for Measuring Productivity in the Canadian Construction Industry: A Systematic Literature Review
Bibliographic record
Abstract
The construction industry is a key driver of Canada's economy, contributing approximately 7.2% to the Gross Domestic Product (GDP) and employing 7.9% of the national workforce, according to Statistics Canada.However, the sector faces a significant challenge: a steady decline in labour productivity, now at its lowest in three decades.One major obstacle in addressing this issue is the lack of a standardized framework for measuring productivity across projects.Inconsistent definitions and measurement practices hinder benchmarking and industry-wide improvement.This study tackles the problem through a systematic literature review, integrating both quantitative and qualitative methods to explore productivity frameworks and Key Performance Indicators (KPIs).The quantitative analysis, conducted using VOSviewer, revealed keyword linkages and research patterns, while the qualitative review examined existing frameworks, KPIs, and benchmarking strategies.Productivity measurement was categorized into three levels-industry, project, and activity-while KPIs were organized into six domains: cost, time, quality, health and safety, labour, and environment.Examples include cost predictability, construction speed, and CO₂ emissions per unit.These indicators are essential for evaluating performance and supporting Canada's sustainability goals.While this study does not develop a new framework, it offers valuable insights for future efforts.It highlights global models like the "A Seven-Step Framework for Success" and "C792" as adaptable tools suited to Canada's unique context, including remote work sites, severe weather, and environmental priorities.By building on these insights, Canada can move toward a standardized productivity framework that enhances benchmarking, boosts sector performance, and strengthens global competitiveness.
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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 | 0.102 | 0.212 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.065 | 0.065 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".