Acquired but not Required?: the Gap between the Competency in Education and That in Action for School Librarianship
Bibliographic record
Abstract
This study confirms the gap between the competency of “school librarian” in education and that in action. The authors carried out the semi-structured interviews with eight persons who had experience of working as a school librarian. The authors asked them whether they thought that it was necessary to learn the competency in the list made by authors, and whether they utilized that at their works. As a result, most of the competencies was required at work, moreover, it was necessary to learn in education. According to their answers, the authors recognized following things. Firstly, there was little need to learn some competencies in the categories of library management and organization of materials. Secondly, the competencies about history, other types of library and school librarianship of foreign countries should be learned in education, though those were not utilized at work. Thirdly, the competency of pedagogy should be added to the list.
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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 | 0.001 | 0.001 |
| 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 | 0.017 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".