Engaging Local Libraries with the New Brunswick Community A Collaborative Case Study Research Initiative: Final Report
Bibliographic record
Abstract
How can libraries deepen engagement with their communities by working with citizens to address challenges related to healthy living? To explore this question, we conducted a project in conjunction with the New Brunswick Free Public Library (NBFPL) in a mid-sized, highly diverse U.S. city (New Brunswick NJ), and community members from representative diverse ethnic groups. Our project aimed to apply the Kettering Foundation’s 6 democratic practices to help libraries engage with citizens from marginalized populations. More specifically, we intended to enhance library-community relationships through community conversations or “forums” that uncovered issues of common concern around health and well being, with a special emphasis on engaging Latino residents of New Brunswick. Each community conversation, or “forum”, offered a situation or meeting in which people could talk about a problem or matter of public interest in a safe, moderated space. Through this case study approach using community conversations focused on health and well being, we examined the process and outcomes of implementing this collaboration and considered implications of this case example for how public libraries serving diverse populations can grow and sustain meaningful connections with the public and foster civic participation. This project was part of a learning exchange with the Kettering Foundation entitled “Libraries and the Public: Returning to Democratic Roots” that focused on exploring ways that libraries can work with ordinary citizens and communities to address issues of common concern. The exchange also included the Multnomah County (Portland OR), Houston, Cincinnati, and Topeka public libraries. The Kettering Foundation conducts research around the question: “What does it take to make democracy work as it should?”
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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.037 | 0.010 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".