Strengthening Urban Food Systems Through Extension Programming and Community Engagement: A Case Study of New Brunswick, New Jersey
Bibliographic record
Abstract
Cooperative Extension (Extension) can, and in many cases already does, engage in well-rooted partnerships with urban audiences. Yet, it is important to recognize that there are many layers to the diversity that exists within urban audiences– there is no single “urban community.” This article presents a case study of food security programming in New Brunswick, New Jersey, including collaborations between Rutgers Cooperative Extension and multiple community organizations to illustrate important considerations for engaging in urban Extension initiatives. Specifically, challenges exist in identifying urban audiences, including those who are hidden, especially as the demographics of city residents can vary greatly within a single geographic area. Solutions include the development of deep community partnerships and creative engagement of university students, with the latter participating as both an audience to benefit from Extension programming and as partners in program implementation. Evaluating urban Extension programming can provide important information as to whether a particular program is meeting the needs of the target audience, but a challenge exists in distinguishing the impact of a single Extension program operating in what is often an ecosystem of programs addressing food insecurity in an urban area.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".