Bibliographic record
Abstract
Edmonton is very lucky for many reasons. Light Rail Transit (LRT) and extensive green space are just two reasons. During the winter months, however, the green belts that surround our neighborhoods and run along our utility corridors become white belts. We’ve seen attempts to battle the cold and recreate warm-weather commuting: increased ped-ways, large malls, heated bus terminals, and other mechanisms. Yet the more we treat winter as something to be accommodated and worked around, the more we retreat, hibernate, and use our cars, the greater our negativity about winter. And so the question remains: How can we shift our thinking around winter and reclaim our abundant public space and corridors in a way that is inherently meaningful for Edmontonians? \nWhat if we could ski to work, or to the LRT? Systems mapping, using rich picture as a design method, helped a group of community enthusiasts understand commuting practices and available green space (white space) in the area. What we found was a vast amount of interconnected white space linked to the train line. The group used causal layered analysis to better understand the systemic causes, worldviews, and mental model underlying why these two transportation systems had never interacted before. As the group dove into an understanding of these two systems, what became clear was a common link around a broader overarching system: winter. The group framed a new mental model for how to tap into this potential. From this premise, a participatory, community based initiative, #Ski2LRT, was formed. \n#Ski2LRT launched as an emergent movement that attempted to shift mindsets around three concepts: Winter, cross-country skiing in urban settings and LRT usage. A simple Facebook page was designed and a cross-country ski rack was placed at the Century Park LRT station. What happened next and the unintended ripple effects went beyond the original intention. It was unknown that neighborhood ski enthusiasts felt isolated. Unintentionally, this initiative connected a community and gave like-minded individuals a space to convene. This initiative and shift impacted the identity of the city and a new municipal group called “SkiWay” formed, connecting the ski clubs in the city, alongside urban transportation initiatives, to reinvigorate cross country skiing in the city.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".