Placemaking through Deep Cultural Mapping: The Where is Here? Project
Bibliographic record
Abstract
Abstract One of the most visible avenues used by small cities to retain competitiveness can be seen in the attempts to revitalize their downtown areas to create places and spaces enjoyed and valued by residents and visitors. Formerly recognized as the heart or centre of small cities, many downtown areas have suffered due to urban sprawl and a loss of connectedness or familiarity among new residents. While efforts to address downtown revitalization are evident such as the creation of public spaces, events and support for small businesses, there remains a need to understand if, and how, residents in small cities value their downtown areas. VIU logo WLCE logo Information A publication of the World Leisure Centre of Excellence © Nicole L. Vaugeois, Sunny Rosser, Sharon Karsten, Alanna Williams and Pam Shaw 2016
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".