Bibliographic record
Abstract
In recent years, construction projects across Canada have encountered significant challenges, including a global pandemic, disruptions in the supply chain, environmental disasters, geopolitical conflicts, war, protests, volatile financial conditions, and the everincreasing time and costs associated with dispute resolution. These challenges have contributed to a scarcity in competitive and reliable pricing under traditional project risk profiles in some regions and sectors. Canadian construction industry stakeholders are increasingly interested in collaborative contracting models like early contractor involvement, progressive design build, integrated project delivery, and alliance models. These models aim to achieve on budget and on schedule project completion, while minimizing disputes and incentivizing cooperative behaviours. Major projects in Ontario and British Columbia are proceeding under collaborative contracting models. Meanwhile, the energy sector in Canada appears to be one of the few industries slow to adopt collaborative contracting models. It is common to see traditional fixed price or costplus design-build or engineering, procurement, and construction contracts on large industrial projects, particularly in Alberta. But the energy sector is not immune to the market factors shifting the tides in various other construction sectors in Canada. If energy projects wish to attract bids from an experienced and varied pool of potential participants and take advantage of some innovative contracting strategies, change may be warranted. This article assesses the features and suitability of these collaborative contracts for the construction of energy-related projects in contrast to more traditional delivery models. The authors provide commentary on their risks and benefits and identify relevant opportunities for the energy sector in Canada to embrace collaborative construction contracting models.
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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".