Literature in Digital Environments: Changes and Emerging Trends in Australian School Libraries
Bibliographic record
Abstract
Igniting a passion for reading and research is core business for school libraries, inevitably placing the library at the center of the 21st century reading and learning experience. It is in this context that digital literature creates some challenging questions for teachers and librarians in schools, while the emergence of digital technology and/or device options also offers a great many opportunities. Collection development in school libraries encompasses an understanding of the need to contextualize these e-literature needs within the learning and teaching experiences in the school. The Australian Library and Information Association’s 2013 statement Future of collections 50:50 predicted that library print and ebook collections in libraries would establish a 50:50 equilibrium by 2020 and that this balance would be maintained for the foreseeable future. This statement from the Australian professional body raised the need to know more about e-collections in school libraries. For teacher librarians in Australian schools, the nature of online collections, and the integration of ebooks into the evolving reading culture is influenced by the range and diversity of texts, interfaces, devices, and experiences available to complement existing print and media collections or services. Management and budget constraints also influence e-collections. By undertaking a review of the literature, a discussion of the education context, and a critical analysis of the trends evidenced by national survey data, this paper presents an overview of the changes and emerging trends in digital literature and ebook collections in school library services in Australia today.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.039 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".