Reading work as a diversity practice: A differentiated approach to reading promotion in academic libraries in North America
Bibliographic record
Abstract
This article positions the practice of working with readers in academic libraries as a diversity practice and examines this practice through the lens of the Diversity by Design concept. We use Diversity by Design to propose and explicate a differentiated approach to reading promotion on campus, drawing attention to the broader and multiple meanings of diversity in the context of reading engagements. We look at the differentiated nature of readerships on campuses as an expression of inherent diversity in North American institutions of higher education and, by extension, academic libraries. We also make specific recommendations on how to give reading practices in academic libraries a boost and a new direction, befitting the diverse and eclectic nature of contemporary North American universities.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.027 | 0.042 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".