Building capacity for equity, diversity, and inclusion in public library programs through community-led practices: an innovation model
Bibliographic record
Abstract
The purpose of this study was to understand how to build capacity among public libraries and librarians for engaging in community-led adult program planning in order to increase equity, diversity, and inclusion (EDI). Community-led methods have been demonstrated to be effective in increasing EDI (Blackburn, 2017; Muddiman et al., 2000; Pateman & Williment, 2013; Working Together Project, 2007), and a variety of models exist to aid librarians in understanding and implementing them (Edmonton Public Library, 2016; Gonz�lez, 2020; Hart, 1992; IAP2, n.d.; Kesseler, 2019; Sonnie, 2018; Seattle Race and Social Justice Initiative, 2012; Sung & Hepworth, 2013; Working Together Project, 2007). However, a community-led programming approach has not been widely adopted by public libraries in the United States. This study used Clark and Estes? (2008) gap analysis framework to explore the barriers to adoption and to develop evidence-based recommendations for increasing capacity. It employed qualitative methods and was informed by a critical participatory action research approach (Kemmis, 2016). Ten librarians from across the United States participated in focus groups followed by reflective journaling. The findings identified assets and gaps in key knowledge, motivation, and organizational influences (Clark & Estes, 2008), resulting in the description of high-capacity and low-capacity models for libraries. Based on these findings and evidence from performance improvement literature, the researcher developed a recommendation for a three-phrase organizational change initiative that public libraries may use to build their capacity for community-led programming.
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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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".