Driving Change: A Model for Collaborative Librarianship in Prince George’s County, Maryland
Bibliographic record
Abstract
The Prince George’s County Memorial Library System (PGCMLS) has a long-standing partnership with the county’s human rights education and enforcement agency, the Office of Human Rights (PGCOHR), formerly the Prince George’s County Human Relations Commission (PGCHRC). The two agencies serve over 967,000 Prince Georgians, a majority-Black (64.4%) and Latin or Hispanic (19.5%) population with a sizable immigrant community (22.7%). The civil rights issues of 2020 hit close to home in Prince George’s County and the agencies have sustained a multi-year effort to provide residents with opportunities to learn how to engage with social justice topics for personal and collective advancement. This paper outlines the agencies’ innovative model for collaborative community programming, which has dramatically expanded the scope and impact of their equity, diversity, inclusion, and antiracism (EDIA) initiatives despite minimal funding resources and the limitations of the COVID-19 pandemic. PGCMLS and PGCOHR’s approach to joint programming is modeled in their Collaborative Programming Lifecycle, which can be applied to a wide range of content areas, whether special events, series, thematic programs, or special events. The lifecycle also touches individual presenters, partners, funders, attendees, and the daily work of programming staff. The partners have successfully deployed the Collaborative Programming Lifecycle to develop internationally acclaimed EDIA programs in multiple formats that influence local efforts to advance social equity and anti-racism. The joint mission of this partnership is to provide meaningful conversation that strengthens the collective community. While this partnership pre-dates both the pandemic and the murders of George Floyd and Breonna Taylor, the agencies rapidly transitioned to virtual programming and engagement during the COVID-19 pandemic. In addition to immediate local impact, the partnership’s programs have resulted in an compelling new model for making local programs accessible to larger communities at state, regional, and national levels.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".