The Portrayal of Librarians in Obituaries at the End of the Twentieth Century
Bibliographic record
Abstract
Obituaries can reveal much about the way a profession is conceived and structured in the popular imagination. This article examines obituaries of librarians in the New York Times between 1977 and 2002 to determine how librarians were presented to the general public by a major newspaper. Although librarianship is a female‐intensive profession, 63.4 percent of the obituaries chronicled the lives of male librarians. Although public and school librarians outnumber their academic counterparts, obituaries focused on academic librarians. Far from creating a stereotypical portrait of librarians as shy, dour, dowdy, and sheltered individuals, the emphasis on large‐scale achievements in the obituaries produces an image of librarianship as a glamorous profession. Some librarians are presented as sleuths and detectives who amassed large collections. They contributed to the progress of scholarly research with extensive publications. Many others had connections to prominent people, making the most of these social networks in their work. Librarians were also players on the global stage, founding libraries abroad and developing international guidelines that led to institutional progress. Emphasis on large‐scale accomplishment, however, tends to obscure the contributions of librarians who daily perform countless small and caring acts that, summed together, positively affect the lives of ordinary individuals.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".