Bibliographic record
Abstract
I gave a PowerPoint presentation of my topic--I had a one-hour time slot. There were approximately 35 people at the session. It seemed to be well received--a number of people cam up to me at the end of the presentation and gave unsolicited thanks and praise. The responses I received indicated that people liked the fact that I had given a “how-to” presentation, and that the information could be used to develop similar activities elsewhere. There seemed to be a need to show people how to use blogging in a language learning/ESL environment. More generally, this presentation and the panel discussion in which I participated as “one of the four Alberta Universities”, both helped me get the message out there that AU does have ESL programming, and that our courses are innovative (more so than many) and on a par as far as general quality is concerned with those offered elsewhere. I was also able to meet with others at the conference who are interested in CALL (Computer Assisted Language Learning). The positive responses I received (there were no negatives) encourage me to continue with the various blogging activities that I have been attempting to introduce to my courses. While the one type of blogging that I have been doing with students will remain essentially the same, I will continue to explore other types of blogging that could be used to assist students with the language learning/writing aspects of the courses that I coordinate.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.013 |
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".