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Record W3197557272

Planning for Uncertainty: Adaptation Strategies for Agricultural Self-Reliance in the Lower Mainland of British Columbia, Canada

2020· article· en· W3197557272 on OpenAlexaboutno aff
James MacDonald-Nelson

Bibliographic record

VenueResearch Repository (Delft University of Technology) · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyFood securityAgricultureMainlandGovernment (linguistics)Mainland ChinaUrbanizationMetropolitan areaEnvironmental planningBusinessFood systemsEnvironmental resource managementEconomic growthEconomicsChina
DOInot available

Abstract

fetched live from OpenAlex

Despite relatively progressive policies put forth by the Canadian government to tackle the challenge of climate mitigation, cities and regions across the country are only beginning to address the equally important task of adaptation. This conversation typically focuses on the spatial adaptability of shorelines, dense urban environments, and the enhancement of green spaces. While these measures are undoubtedly important to implement, there is a lack of public awareness regarding how our region’s food systems and the agricultural landscapes that sit on edge of cities across Canada, must also adapt. Agriculture is a key part of the Canadian economy, both in the export of products grown domestically and the importation of products from the United States, Mexico, and Asia. This reliance on imported food is a standard part of the food supply chain in Canada. However, with a changing climate affecting places like California (where a lot of fresh produce is imported from) and global challenges such as the COVID-19 pandemic, these supply chains are becoming more vulnerable to unexpected disruptions. This, in turn, threatens the food security of all Canadians. This thesis explores the adaptive potential of the local and regional food system in Canada’s only urbanized delta region, the Lower Mainland of British Columbia. As with all delta regions around the world, this low-lying metropolitan region will increasingly face many challenges related to flooding, ongoing urbanization, and the unpredictability of extreme natural disasters that threaten communities and food production. The Lower Mainland is one of the most agriculturally dynamic regions in Canada and is unique as a considerable amount of arable and cultivated land is integrated closely with the urban fabric of the territory. However, with decreasing self-reliance in regional food production and an over-reliance on cheap imported products, the Lower Mainland is at a crossroads when it comes to the future of its food system. The intent of the design proposal is to find ways in which to rebalance regional food cultivation by expanding how and where production takes place. Integrating agriculture tightly within communities and using it as a catalyst for new public spaces, urban development, and agri-tech innovation along a key regional corridor will serve as the basis for the design exploration. The objective of this thesis is therefore to demonstrate how the spatial and functional organization our food systems can, and must, adapt given the uncertainty of our collective future. Vulnerable supply chains, unexpected disasters and shifts in the global economy significantly risk our ability to adequately feed people. If we take this issue seriously and begin planning for an uncertain future by first addressing what makes us most vulnerable, we can begin to adapt and build the capacity to face these challenges with confidence.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.666

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0120.002
Scholarly communication0.0060.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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.

Opus teacher head0.026
GPT teacher head0.221
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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