Good Roads 2.0: An Analysis of the Impacts of Rail-Trail Organizations on Strategic Planning, Community-Building and Economic Revitalization
Bibliographic record
Abstract
Friends of Rails to Trails Vancouver Island (“FORT-VI”) seeks to develop a 224km rail-trail corridor from Victoria, British Columbia (“BC”) to Courtenay, BC, with an additional spur from Parksville to Port Alberni. To advance and manage this goal, FORT-VI asked for a comparative analysis of five different rail-trail initiatives that outlines the potential and likely impact, challenges or barriers that stand in the way of developing a rail-trail corridor, and smart practices or successes of similar projects around the world. Influenced by Bryson’s (2018) strategic change cycle, this paper identifies potential outcomes of rail-trails initiatives across multiple policy areas that include: health, recreation and ecological economics; and land use, reconciliation and governance. The analysis demonstrates that, while FORT-VI’s initiative may be suspended indefinitely due to external influences, there is much information to be gleaned about the value of rail-trails across all policy areas, which can assist FORT-VI in its continued advocacy for a rail-trail on Vancouver Island. Not only can this information support the development of rail-trails like FORT-VI’s Island Rail Corridor, but it can also benefit other areas that are looking to develop rail-trails. Lastly, it can assist various associated actors, such as First Nations in the Vancouver Island area, who may be interested in supporting a rails-trails initiative or learning more about such initiatives in general.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".