Toward a participatory VGI methodology: crowdsourcing information on regional food assets
Bibliographic record
Abstract
Local knowledge has been underrepresented in food-related policies and planning. The goal of this research was to engage members of a local food community and generate volunteered geographic information (VGI) on community food assets. During active data collection, over 200 food assets were reported. This paper details the systematic approach used to create VGI, which emphasizes the socio-cultural context surrounding the mapping technology. The project began with an identified need to connect to and learn from the local food community. The participants were drawn from active food system stakeholders, and a Geoweb infrastructure was selected based on publicly available crowdsourcing tools. The resulting VGI is presented according to system functions: input (Web traffic, contributors, input types), management (contribution vetting, privacy), analysis (typology of input), and presentation (sharing the submitted data). Despite limitations, this study revealed a hyper-local and community-driven perspective on food assets, opened access to government and private data, and increased the transparency and accessibility of information on the regional food system. This research also revealed that there is a growing need for intermediaries who can bridge the gap between experts in the subject matter and experts in digitally enabled participation, and a need for non-government open data repositories.
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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 | 0.064 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".