Bibliographic record
Abstract
You can’t teach people everything they need to know. The best you can do is position them where they can find what they need to know when they need to know it. (Seymour Papert) For school librarians, this is certainly part of a core responsibility, to provide students with digital literacy skills and strategies that will enable them to find and access information at point of need, in order to create knowledge (Farkas, 2011). While students are growing up in this digital age, research reveals they are not necessarily skilled in reading to locate and use online information effectively (Leu, Zawilinski, Forzani, & Timbrell, 2014b; Pickard, Shenton & Johnson, 2014). This is significant when “students overestimate their ability to engage with information in a critical and literate manner” (Kirkwood in Beetham & Oliver, 2010, p.162). Yet, students are required to be ethical and critical thinkers, and engage as collaborators and creators in participatory digital environments (Coiro, 2003; Mackey & Jacobson, 2011; Association of College & Research Libraries (ACRL), 2015). This exploratory case study seeks to investigate Year 5 students’ (ages 10-12) learning experiences within a school library program. It endeavoured to explore the pedagogical background, motivation and steps in implementing digital and information literacies. Did these sessions provide students with the emergent skills and strategies to support independent research and collaborative inquiry as they began their International Baccalaureate Primary Years Programme (IB PYP) Exhibition?
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".