A tale of two trails: Lessons from a comparative account of the Trans Canada Trail and the Sendero de Chile
Bibliographic record
Abstract
A new generation of greenways has emerged in recent years, the most ambitious of which are nationwide, interconnected networks of multi‐use, multi‐purpose greenways and trails, clustered under a single national vision. However, because these initiatives have been the focus of so few research studies, opportunities to glean lessons from their planning and implementation have been limited. This paper contributes to addressing this knowledge gap by presenting a comparative account of two networks, the Trans Canada Trail in Canada and Sendero de Chile in Chile. Using document analysis and interviews with officials closely involved in their development, the evolution of both networks is documented over time, emphasizing similarities and differences related to their planning and implementation. Both initiatives have faced significant challenges in reaching their connection goals and have availed themselves of a diverse range of opportunities and strategies to advance their agendas. A simple model of a virtuous cycle is proposed to highlight the positive feedback—between political and public support, sustained funding, partnership development, accessibility for urban residents, and connectivity of the network—generated by sustained network expansion over time. It is hoped that the insights offered from this analysis may offer guidance to inform the development of similar scale projects elsewhere.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".