Bibliographic record
Abstract
In 2023, Northern Policy Institute published a report concluding that 2+1 roads offer a more cost-effective approach to upgrading major highways in Northern Ontario than highway twinning. 2+1 roads are three-lane roads with one lane in each direction and a passing lane alternating direction every few kilometres, along with a median barrier separating the two directions of traffic. The 2+1 model offers similar safety benefits to highway twinning but is less expensive because it does not require the construction of a second, parallel road. By offering more frequent and safer passing opportunities and reducing the number of road closures due to collisions, 2+1 roads can also improve traffic flow on transportation routes that are critical for rural and northern communities and national supply chains. Tested and proven internationally, 2+1 roads can represent the ideal road configuration on highways across rural and northern Canada where traffic levels are too high for a two-lane road but too low to justify the cost of a divided, four-lane highway. Drawing on the NPI publication, this presentation will discuss how 2+1 roads work, their advantages over alternative road configurations, and the locations where they tend to work best.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".