Bibliographic record
Abstract
This project's aim is to examine how communities around the edge of the Tokyo metropolitan area have and have not incorporated agriculture as they grow and develop, and to help Canadian communities learn from these same lessons as our cities grow. The scale will be taking a look at areas that are already developed, not the far edges of the metropolitan area. Six key attributes for examination and consideration will be the development pattern of public versus private train lines (if one has more agricultural spaces in communities sounding the stations); Peri urban fringes as the centre of (potential) agrotourism; Peri urban spaces as contemporary third places for sub-urban residents in their communities; Economic development opportunities in Peri urban space, that stem from proximity to farms including restaurants and markets; Do peri urban farms fill the same role rural farms do; and how have these spaces avoided development into housing the way the land around them has? The project will review current land use in Tokyo, as well as what protection and policies currently exist for the remaining agricultural land. Attention will also be paid to what is not taking place in these spaces, to see what drawbacks exist. Interviews with stakeholders, and farm owners especially will look to uncover if these spaces exist in a viable independent state, or if they occur covered losses for various reasons. Finally, an analysis of the methods used by Tokyo's integration of rural spaces to create mixed used suburbs (and their transferability).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".