Building beyond barriers: a study of technology integration in Northern Canada’s construction industry
Bibliographic record
Abstract
The construction industry in Northern Canada (NC) faces unique challenges, including extreme weather conditions, limited daylight, and remote geographical locations. These factors significantly impact project timelines, costs, and overall efficiency. Novel technological and management solutions in the construction sector offer promising solutions to overcome these barriers. This research used a questionnaire survey and semi-structured interviews to investigate state-of-the-art technical and managerial solution adoption, key challenges, and critical success factors for improved project delivery in NC. Except for modular construction, other identified technologies, particularly information technology-based solutions, were revealed to be less used. Over 80% of the challenges have been identified as important issues. All the respondents agreed that identified critical success factors (CSF) are vital for improving construction project delivery. The findings of the semi-structured interviews confirmed the above results, and skill shortage was revealed as a central challenge as well as a CSF for infrastructure project delivery in NC.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".