How the image drawing method can act as an alternative barometer of librarian instruction
Bibliographic record
Abstract

 
 
 Previously, I examined changes in pictures of school libraries drawn over time by university students in a teacher training program taking a course on the importance of school libraries. The results revealed an increased tendency to depict librarians; even so, librarians featured in only 12 of 32 pictures. This study compares my results with those for similar teacher and teacher librarian courses by other teachers and (in most cases) at other universities. Besides my course, only 1 of 15 other courses revealed an increased tendency to draw a librarian, with no significant differences in proportion of students who depicted librarians among the courses, revealing that my lectures successfully communicated the importance of school librarians. Also, 4 of 11 courses that focused on information media revealed an increased to draw PC(s). These results show that the image drawing method may suffice as an alternative barometer for librarian instruction.
 
 
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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 | 0.001 |
| 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 | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".