Multi-purpose greenways and nationwide trails networks: An examination of the Trans Canada Trail and the Sendero de Chile
Bibliographic record
Abstract
Greenways and trails have emerged in recent decades as a mechanism to facilitate access for increasingly urban-based societies to nature and its related services. Among the most ambitious of these initiatives are nationwide, interconnected networks of multi-use, multi-purpose greenways and trails, clustered under a single national project idea/vision, such as the Trans Canada Trail (TCT) in Canada and Sendero de Chile (SDC) in Chile. Unfortunately, limited research has been conducted to document the development of these national scale initiatives or glean lessons from their experiences. This thesis contributes to this knowledge gap by analysing these two national scale initiatives. Using document analysis and interviews, the evolution of the TCT and SDC networks is documented over time, emphasizing similarities and differences between them as well as identifying challenges and opportunities related to their implementation. Both initiatives have faced significant challenges in reaching their connection goals but have availed of opportunities, such as different strategies of multi-level and multi-stakeholder collaboration and partnership to advance their agendas. A virtuous cycle is recognized in relation to the positive feedback generated by sustained network expansion over time. It is hoped that the insights offered from this thesis may offer guidance to inform the development of similar projects elsewhere, particularly in less developed countries.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".