May the Bun Be With You: An Annotated Bibliography of Librarians and Their Image
Bibliographic record
Abstract
A very real tension exists between librarians’ attempts to alter their image(s) and the popular press’ and the public’s lingering preference for Marian the Librarian similes. As the Hot Picks @ Your Library calendar indicates, librarians take creative opportunities to dissuade the public of the “image of a dourfaced matron behind a forbidding desk” (Gillespie 2003, A01). But how do librarians attempt to frame their own discussion of the classic caricature? The authors wanted to know how librarians themselves have considered and researched the impact of the stereotype on the profession. What follows is a literature review of materials published over the past 20 years. These materials span the gamut of libraries and librarians, from the real to the imagined, including materials such as students’ perceptions of academic librarians, the public’s misconceptions of librarians in Canadian public libraries, and portrayals of librarians in fiction. By reviewing materials published over a 20-year time period, the authors have captured a microcosmic glimpse of the changing image(s) of librarians.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.023 | 0.041 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".