Conversations on Education with Gary Poole and Michael McManus
Bibliographic record
Abstract
It is, perhaps, understating it to say that today’s tertiary education landscape has changed. If you talk to professors on most campuses, they are likely to bemoan the fact that students seem to be more demanding in the way they approach their educational experience. If you speak to students, they are equally likely to say that they expect to get ‘more’ out of their university experience. Employers and alumni, on the other hand, are increasingly providing feedback to universities about the kind of graduates they wish to see in the workplace. Furthermore, if you venture into cyberspace, there seems to be ever more online debates about all kinds of issues relating to education. With the ‘buzz’ generated in the past year alone by Coursera and EdX, the question of the place of technology in our classrooms has assumed a sense of real urgency. Given the increased level of conversation about education, technology and 21st century critical literacies, many of us working in institutions of higher learning (IHL) now find ourselves constantly confronted with questions like: What critical abilities do our students need that will equip them for the 21st century global economy? What kind of education experience can we offer our students that will provide relevant skills for the workplaces of tomorrow? What is the place of technology in tertiary institutions? How can education be transformative? (Abstract taken from first paragraph of document)
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.046 | 0.010 |
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".