Bibliographic record
Abstract

 
 
 This paper will describe assessment tools used by the author as a primary school librarian practitioner. It will show: how the tools can be used to provide the School Librarian with the means to report on students; and how the School Librarian can show that learning has taken place in the library. In the process the credibility of the School Librarian as a faculty member is enhanced as well as the collaborative partnership with staff and as a consequence has a strengthened position for advocacy for the place of the library and the school library as a key contributor to the school mission. Thus it will be shown that assessment is one avenue that the School Librarian can demonstrate his/her professionalism and that she/he is responsible for providing ‘the school library as a learning environment’. This paper thus contributes to an aim and a subtheme of the 44th IASL Conference.
 
 
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.017 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".