More Than Mapping: Improving Methods for Studying the Geographies of Food Access
Bibliographic record
Abstract
More Than Mapping: Improving Methods for Studying the Geographies of Food Access Jerry ShannonPhD, Ashantè M. ReesePhD, Debarchana GhoshPhD, Michael J. WidenerPhD, and Daniel R. BlockPhD Affiliation Jerry Shannon is with the Departments of Geography/Financial Planning, Housing, and Consumer Economics, University of Georgia, Athens. Ashantè M. Reese is with the Department of African and African Diaspora Studies, University of Texas at Austin. Debarchana Ghosh is with the Department of Geography, University of Connecticut, Storrs. Michael J. Widener is with the Department of Geography and Planning, University of Toronto, Toronto, ON, Canada. Daniel R. Block is with the Department of Geography, Sociology, History, and African-American Studies, Chicago State University, Chicago, IL. CopyRightCorrespondence should be sent to Jerry Shannon, University of Georgia, Department of Geography, 210 Field St, Room 204, Athens, GA 30602 (e-mail: jshannon@uga.edu). Reprints can be ordered at http://www.ajph.org by clicking the "Reprints" link. CONTRIBUTORS J. Shannon conceptualized the original focus of the article and coordinated revision. All authors contributed to its drafting and to the revision process. https://doi.org/10.2105/AJPH.2021.306339 Accepted: April 12, 2021 Published Online: August 31, 2021
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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 | 0.061 | 0.364 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".