Bibliographic record
Abstract
The photos include (clockwise from top): 1 City of Des Moines' (Washington) Healthy Des Moines Initiative staff and key stakeholders meet with urban planning consultants to discuss a land use assessment of healthy food access for inclusion of healthy eating goals, policies, and strategies in the comprehensive plan. Names and affiliations (left to right): Branden Born, associate professor, Department of Urban Design and Planning, University of Washington (see his contribution in this issue's Preparing Future Food System Planning Professionals and Scholars: Reflections on Teaching Experiences); Kara Martin, urban planner, Urban Food Link, LLC; Brice Maryman, landscape architect, SvR Design; Amalia Leighton, civil engineer, SvR Design; Barbara Houston-Shimizu, executive director, South King County Food Coalition; Kim Richmond, volunteer project manager, Daisy Sonju Community Garden & Pea Patch; Laura Techico, land use planner, City of Des Moines; Denise Lathrop, planning manager, City of Des Moines; Eva Ringstrom, graduate research assistant, Department of Urban Design and Planning & Evans School of Public Affairs, University of Washington; Sean Keithly, urban planner, CollinsWoerman. Photo by Sue Anderson, policy analyst, Healthy Des Moines Initiative director, City of Des Moines, Washington. 2 Planner Jenna Silcott administers a rapid market survey at a farmers' market on the Mississippi Gulf Coast. Visitors to the farmers' market were asked about how often they come to the market, whether they will do additional shopping in the area, and how far they live from the market. See Evaluating Food Systems in Comprehensive Planning: Is the Mississippi Gulf Coast Planning for Food? in this issue. 3 John Lubczynski at the St. Jacobs Farmers' Market in the Township of Woolwich, Ontario, in September 2011. John works as an urban planner at the Regional Municipality of Waterloo, Ontario, Canada, and helped draft the food system policies in the new Regional Official Plan. John co-authored Incorporating Policies for a Healthy Food System into Land Use Planning: The Case of Waterloo Region, Canada in this issue. 4 Jerry Kaufman (emeritus professor, Department of Urban and Regional Planning, University of Wisconsin–Madison) with Kami Pothukuchi (associate professor of urban planning, Wayne State University) at Wayne State in August 2011. See their contributions in this issue's Preparing Future Food System Planning Professionals and Scholars: Reflections on Teaching Experiences.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.893 | 0.778 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".