Participatory mapping of regional food assets using volunteered geographic information systems
Bibliographic record
Abstract
Local food systems are increasingly being studied in response to the threats imposed on global agri-industrial food systems. Central to local food is the community who is imaging and implementing diverse and hyper-local food assets, which are making a significant, but largely unknown, contribution to food security, resiliency, and sustainability. It is important to align these assets with broader regional food policies, programs, and regulations. However, there are few mechanisms to engage stakeholders or share local information. One possible mechanism to learn about local food assets is volunteered geographic information (VGI); a phenomenon that blends crowdsourcing, citizen science, and online mapping. It is currently being studied for its ability to engage and gather information from diverse and under-represented groups. This policy-relevant research investigates how VGI can support greater engagement and knowledge sharing across diverse food stakeholders. To achieve this objective, the VGI system framework is established to study the processes that support the creation of VGI. Next, the new era of food mapping, dubbed Food Mapping 2.0, is investigated to understand the impact evolving mapping techniques have on the engagement of food stakeholders. Lastly, the VGI systems framework, which is embedded in participatory geographic information system and participatory action research methods, is applied to support participatory mapping of regional food assets in Durham Region. His research gathered contributions on over 200 food assets in Durham Region – an upper-tier municipality just east of Toronto consisting of eight lower-tier municipalities – effectively capturing the distributed intelligence of government, not-for-profit, and community stakeholders. The crowdsourced data include locations, descriptions, and media related to farms, markets, community gardens, foodscapes, and other innovative food assets. The community identified urban food assets as a central strength of the regional food system. Overall, this project enabled the creation of an open food assets dataset, further supporting the development of an online Food Assets map and a Crowdsourcing Urban Food Assets report, which are collective used to inform future food policy, regulation, and program development. Overall, this research revealed a uniquely local and community-driven perspective about food system assets within Durham, while serving as a prototype of the VGI systems framework.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".