Enabling School Librarians to Serve as Instructional Leaders of Multiple Literacies
Bibliographic record
Abstract
Research has demonstrated that school leaders have little to no understanding of the instructional leadership role of the school librarian and have received little to no training in how to lead this population (Lewis, 2018; 2019). Though the standards of the school library field state that school librarians should be equipped and able to serve as instructional leaders of multiple literacies in K-12 education, barriers exist that inhibit this from becoming a reality in many schools. One of these barriers is a lack of administrative support in the form of a district library supervisor to develop a vision for and provide support to the district’s school library program and its personnel. Very little research has been conducted to examine the support needs of in-service school librarians (Weeks et al., 2017), and no research has been conducted to explore how to equip existing leadership to effectively lead its population of school librarians in a school district that lacks an official district library supervisor. The purpose of this study is to explore how school district leaders can foster the development of an effective school library in which school librarians serve as instructional leaders of multiple literacies.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".