BUILDING SUSTAINABLE FOOD SYSTEMS) URBAN FOOD STRATEGIES IN AMSTERDAM AND
Bibliographic record
Abstract
5 Introduction 5 Roadmap 10 Methodology 10 1: Urban Food Strategies: A definition 10 1.1 Bringing food on the urban agenda: Toronto 11 1.2 Attaining food justice: Belo Horizonte 13 1.3 Feeding a metropolis: London 14 1.4 Urban Food Strategies defined 16 2: Case studies in the Netherlands: Urban Food Strategies in Amsterdam and Utrecht........16 2.1 Setting the scene 16 2.2 Proeftuin Amsterdam 17 2.2.1 History 17 2.2.3 Actors 18 2.2.4 Goals 20 2.2.5 Activities 22 2.2.6 Some outcomes 25 2.2.7 Continuing the spirit of the project 27 2.2.8 Conclusion 28 2.3 Lekker Utregs 29 2.3.1 History 29 2.3.3 Actors 31 2.3.4 Goals 34 2.3.5 Activities 34 2.3.6 Outcomes 35 2.3.7 Future perspectives 36 2.3.8 Conclusion 36
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.005 |
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".