Gender, Race, and Age of Librarians and Users Have an Impact on the Perceived Approachability of Librarians
Bibliographic record
Abstract
Objective – To assess how the age, gender, and race characteristics of library users affect their perceptions of the approachability of reference librarians with similar or different demographic characteristics.
 
 Design – Image rating survey.
 
 Setting – Large, three-campus university system in the United States.
 
 Subjects – There were 449 students, staff, and faculty of different ages, gender, and race.
 
 Methods – In an online survey respondents were presented with images of hypothetical librarians and asked to evaluate their approachability, using a scale from 1 to 10. The images showed librarians with neutral emotional expressions against a standardized, neutral background. The librarians’ age, gender, and race were systematically varied. Only White, African American, and Asian American librarians were shown. Afterwards respondents were asked to identify their own age, gender, race, and status.
 
 Main Results – Respondents perceived female librarians as more approachable than male librarians, maybe due to expectations caused by the female librarian stereotype. They found librarians of their own age group more approachable. African American respondents scored African American librarians as more approachable, whereas Whites expressed no significant variation when rating the approachability of librarians of different races. Thus, African Americans demonstrated strong in-group bias but Whites manifested colour blindness – possibly a strategy to avoid the appearance of racial bias. Asian Americans rated African American librarians lower than White librarians.
 
 Conclusion – This study demonstrates that visible demographic characteristics matter in people’s first impressions of librarians. Findings confirm that diversity initiatives are needed in academic libraries to ensure that all users feel welcome and are encouraged to approach librarians. Regarding gender, programs that deflate the female librarian stereotype may help improve the approachability image of male librarians. Academic libraries should staff the reference desk with individuals covering a wide range of ages, including college-aged interns, whom traditional age students find most approachable. Libraries should also build a racially diverse staff to meet the needs of a racially diverse user population. Since first impressions have lasting effects on the development of social relationships, structural diversity should be a priority for libraries’ diversity programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.167 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".