Mind the Gaps: School Librarians' Job Descriptions and the Professional Standards for School Librarians in the United States
Bibliographic record
Abstract
While previous studies have analyzed the contents of different librarians’ job descriptions (Brewerton, 2011; Park, Lu, & Marion, 2009), school librarians’ job descriptions have not received similar attention. The purpose of this study was to compare how well the performance responsibilities from Florida school librarians’ job descriptions agreed with the performance responsibilities from the American Association of School Librarians’ (AASL) professional standards outlined in their publication Empowering Learners: Guidelines for School Library Programs (AASL, 2009). Rates of agreement were calculated by using thematic qualitative content analysis to compare the subjects and actions of the respective performance responsibilities. Analysis showed the ages and origins of the job descriptions did not have a consistent influence on rates of agreement, though job descriptions within the range of 11-20 performance responsibilities tended to have higher average rates of agreement. The various aspects of school librarians’ roles as described in Empowering Learners were present in their job descriptions to different extents, with some aspects more frequently represented than others. The differences between the performance responsibilities in school librarians’ job descriptions and Empowering Learners may be a source of role ambiguity, conflict, erosion, and overload for school 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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".