Bibliographic record
Abstract
The aim is to find and analyse the localized trends of urban agriculture that are taking place in Vancouver and then categorize them according to their efficiency, viability and long-term potential, as we are looking at a 2040 time frame, with their barriers and enablers. Literature reviews were executed to understand theory, ideology, concepts and local trends from periodicals and websites to gain an extensive understanding of urban agriculture in the wide scope. Urban agriculture within the city of Vancouver is struggling to find a long-term foothold due to many issues including: Overuse/misuse of technology, Funding (underfunding or poor budgeting), Community attitudes, Lack of knowledge about agriculture/food safety, Long-term, sustained relevance, Bureaucratic struggles and Lack of usable space in terms of a growing city. Having stated the barriers urban agriculture trends are facing in Vancouver for 2040 I would recommend focusing on those trends that emphasize: strong community involvement through social media and programs, therefore securing the long-term commitment of the people supporting and encouraging them; educational programs that teach the community to overcome their fears of agriculture and prepare them to individually explore farming; movable or adaptable planting spaces with technology and permaculture models that complement yet do not detract from the purpose at hand; and alternative food assets such as farmers markets and kitchens that complement the agricultural processes and reach out to the community also maintaining the profit cycles and closed-looped energy systems. Having community gardens incorporate these trends in manners such as SOLE food has, but taking them a step further to include other spaces and technological benefits would be ideal.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".