Bibliographic record
Abstract
ii Scant research has explored how professors in Canadian universities use Twitter as a teaching tool or to augment knowledge about their subject disciplines. This case study employed a mixed-method approach to examine how professors in an Ontario university use Twitter. Using a variation of the technology acceptance model, the survey (n = 17) found that professor participants—41.2 % of whom use Twitter—perceive Twitter as somewhat useful as a teaching tool, not useful for finding and sharing information, and not useful for personal use. Participants ’ gender and number of years teaching are not indicators of Twitter use. Furthermore, the level of support from peers and the university may be reasons why some do not use Twitter or have stopped using Twitter. Face-to-face interviews (n = 3) revealed that Twitter is not used in classrooms or lecture halls, but predominantly as a means of sharing information with students and colleagues. Another deterrent to using Twitter is not knowing who to follow. Findings indicate that some professors at this university embrace Twitter, but not necessarily as an in-class teaching tool. The challenge and the advantage of using Twitter is to discover and follow people who tweet material and to select relevant material to pass along to students and colleagues. Professor participants in the study found a use for the social network as a means to increase student engagement, create virtual information-exchange communities, and enrich their own learning. iii
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.068 | 0.007 |
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".