Bibliographic record
Abstract
In 2020 major changes took place at Canadian colleges and universities in response to the pandemic, one of these being a shift toward offering all courses online. Before the pandemic, many higher education institutions were already on a clear trajectory to offer more online learning. According to a public report, by 2018, 80% of colleges and 90% of Canadian universities offered distance education, and 98% offered online courses. Changes to online learning have required changes for the roles of academic librarians – not the least of which are new pedagogies for online and open learning. This paper describes findings from a survey of Canadian academic librarians capturing the realities of their online roles, including the pedagogical knowledge and technology skills required. Research findings indicate that academic librarians have varied online learning roles, working across a range of online learning environments and teaching with technology, which requires significant technology and pedagogy competencies. This research has led to the development of a competency framework for academic librarians which indicates that librarians needed blended skills to teach on a continuum from physically co-present to fully online environments. This research identifies key pedagogical and instructional design skills needed as online learning alternatives in post-secondary institutions expand.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
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".