"Can you hear me, Hanoi?" Compensatory mechanisms employed in synchronous net-based English language learning
Bibliographic record
Abstract
writes that the ability to remove the constraints of time and place is a major hallmark of computer-mediated communication (CMC) but that it also supports real-time synchronous forms of interaction. He suggests that "synchronous technologies create a strong network bond because each of the participants must be present at the same time in order to communicate" (Ahern, 2008, p. 99). Kenning (2010, p. 6), expanding on the work of Ciekanski and Chanier (2008, p. 173) would have us view the synchrony and asynchrony as a matter of degree where "face-to-face offers greater simultaneity than audio networks, audio than textchat and text chat than a shared word processor." At Dalarna University, Sweden, we offer modes of communication at many points of Kenning's continuum with a web-based learning platform, including asynchronous document exchange and collaborative writing tools, e-mail, recorded lectures in various formats, live streamed lectures with the possibility of text questions to the lecturer in real time, textchat, and our audiovisual seminars in Marratech or Adobe Connect. Our online students live in many countries around the world and come to our online learning spaces from profoundly different physical realities, so the synchronous seminar is a shared experience that is quite separate from the physical environment in which the students find themselves.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".