Bibliographic record
Abstract
Digital learning objects are new kinds of resources which teacher librarians will be required to manage and make accessible to teachers and students. In Australia there are currently large-scale national and state initiatives underway to develop a critical mass of learning objects. The development of a Learning System Architecture has also become a vital step to make it possible to manage these learning objects. Packages that will enable students and teachers to communicate, collaborate, locate and access resources within intellectual property arrangements, assemble digital resources into learning sequences, assess and report are all necessary requirements. The Learning System Architecture emerging in Australia enables these disparate systems to function together as seamless and interoperable packages. A new profile of teacher librarian competency is being developed in Tasmania to assist with planning the professional learning needs of this group. The new profile includes understandings and experience of information communication technologies and online learning. Managing these new digital resources to support the teaching and learning is a key new professional role for teacher librarians.
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.019 | 0.029 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.023 |
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".