School Library: How to Break the Walls
Bibliographic record
Abstract
The present paper analyzes the possibility of the school library breaking the four walls to which we are used to, using data collected during field research for a doctorate research, from observation and interview with students. Three schools in Belo Horizonte city were researched, one of which really broke the walls of the library, taking the collection to the classrooms. Preliminary observation data showed significant differences among the use of the three spaces, which became clearer in the interviews. The results showed that the differential is, however, in the performance of the professional present in this place. It was evident that the school library should concern about the professional, regardless of the space. Breaking the walls of the library was not the most effective solution to bring students closer to reading and the available materials. So, it is up to the librarian, and not the collection, to break the barriers.
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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 | 0.016 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.021 | 0.030 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 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".