The Case of Teacher-Librarianship by Distance Learning at the University of Alberta, Canada
Bibliographic record
Abstract
The online distance education program, Teacher-Librarianship by Distance Learning, was developed and implemented in the Department of Elementary Education at the University of Alberta, Canada beginning in 1996. At the time, neither the university nor the department had the interest, funding or infrastructure required for such an undertaking, but these developed over time through a combination of careful planning and serendipity. The program’s instructional team has utilized various approaches to establish, maintain and continue the program: a distance education theoretical framework, analysis of distance education research, one-time government incentive funding, and on-going policy relevant research and evidence-based practice. Current challenges facing the organization are program growth, new and emerging technologies, and maintaining flexibility. The solutions to these challenges include a cohort model for the majority of program delivery; a stand-alone course introducing new and emerging technologies as a launching pad for integration of these technologies; and graduate certificate programs for meeting the short term needs of teachers new to the field.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.040 | 0.012 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 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".