Creating a Resilient Regional Food System
Bibliographic record
Abstract
Thousands of people in the Portland Metro region face the crisis of food insecurity every day. Emergencies like the pandemic or extreme weather events only worsen this crisis. How can we develop a truly resilient food system that can withstand shocks and reduce hunger and vulnerability in our region? Join us to hear from frontline-serving organizations, food growers/producers, government representatives, and researchers on this critical issue, and to share your perspective. Presentation of preliminary research findings by Dr. Megan Horst, Meg Grzybowski, and members of the community advisory board* on "Perspectives from Frontline Organizations in the Portland Metro Region on Addressing Food Insecurity During the Covid-19 Pandemic." Followed by a panel discussion with: Carol Chang, Regional Disaster Preparedness Organization (moderator) Sonya McCormick, Oregon Emergency Management Malcolm Shabazz Hoover, Black Futures Farm Michelle Week, Good Rain Farm Dr. Kimberly Zueli, The Feeding Cities Group *Research Community Advisory Board members: Stephanie Clark (Haynes), Vancouver Farmers Market Gloria Lee, Community for Positive Aging Jacobsen Valentine, Feed the Mass
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 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".