Fresh Ideas: Pop-ups and Planning for the City of Calgary Farm Stand Program
Bibliographic record
Abstract
Direct food distribution such as farmers markets, farm stands, and u-pick stands are alternatives to conventional food models like grocery store chains. The City of Calgary’s Farm Stand Program aims to provide places, in the form of pop-up stands, to sell fruit and vegetables to simultaneously support local farmers and offer improved resident access to healthy food options. This report assesses and evaluates the successes and challenges of program implementation and illuminates the role of municipal governments in direct food distribution. By using a case study methodology, four research questions focus on a single case and answer the following: 1) Why and for what purpose was the Farm Stand Program created in Calgary? 2) How has the Farm Stand Program been implemented in Calgary in terms of locations, scope, and objectives? 3) What challenges and strengths have characterized the implementation of the Farm Stand Program in Calgary? and 4) To what extent is the Farm Stand Program accessible to socioeconomically deprived communities? Research questions are answered through spatial analysis, document analysis and key informant interviews. Eight recommendations emerged from the research and offer key insights for planners on how to implement direct food distribution models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.003 |
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".