Bibliographic record
Abstract
Collaboration is the key for a school librarian to work successfully at integrating information skills into the school curriculum and to become a vital cog in the teaching and learning cycle within the school. This is easily said, but how do we make it happen? What strategies can we use to develop opportunities for collaboration with teaching staff? How can we foster strong links across the whole learning community of the school? This paper will briefly consider a definition of collaboration and various models of collaboration including their theoretical and pedagogical underpinning. In addition to considering the role and mindset of the teacher librarian, a range of practical macro- and micro-strategies for developing collaboration with teaching staff in an effective and integrated way will be presented; these include technology, special learning needs, building a reading culture, literacy and instructional design. A self-diagnostic tool developed from this paper is offered to enable each teacher librarian to evaluate opportunities for furthering collaboration in his/her school context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.030 |
| Scholarly communication | 0.029 | 0.045 |
| Open science | 0.003 | 0.034 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.038 | 0.014 |
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".