Made in the MRA: How the Metropolitan Region of Amsterdam could source a quarter of its food from the region by 2030
Bibliographic record
Abstract
This master's thesis explores four future scenarios for short food supply chains in the Metropolitan Region of Amsterdam in 2030 and their potential spatial impact. Agricultural land in the Netherlands has been decreasing in recent decades, especially within the Metropolitan Region of Amsterdam. This trend is likely to continue in the coming decades (Planbureau voor de Leefomgeving, 2015; Lesschen et al., 2020). Alongside this, there is an ambition from both regional government bodies and non-government actors to source twenty-five percent of the food consumed from their own region. This research looks at opportunities for achieving this ambition and how the objectives can be accomplished despite the scarce space in the metropolitan region. Four future scenarios have been drawn up for 2030, based on two axes, with each scenario describing the measures that can be taken to achieve this future scenario. The first axis is focused on governance of the food chain: either by a strong directing government or by polycentric networks. The second axis addresses the use of space: either conventional agricultural land use remains dominant, or alternative forms of food production break through and change the demand for space. By describing the four future scenarios, this master's thesis attempts to highlight multiple paths to the different future scenario’s and to map the possibilities for short food supply chains for the future. As such, this thesis falls within the discussions surrounding the alternative food geographies paradigm. <br/><br/>Key words: urban food systems, short food supply chains, alternative food geographies, Metropolitan Region of Amsterdam, futuring, backcasting, polycentric governance, 2030
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".