Bibliographic record
Abstract
<strong>TALON Online Education Card Game</strong> With the Covid-19 pandemic, online education has gained momentum. The TALON Online Education Card Deck is designed to help you start a conversation about teaching and learning in the digital classroom. <br> The card deck builds on extensive research on online education - and the Voices collected to explore the possibilities and challenges of teaching and learning remotely. <br> It is particularly useful for faculty interested in advancing online education - and their own teaching practice. <br> TALON: https://taloncloud.ca/ Card Deck: https://taloncloud.ca/TLN-Card-Deck <br> References Abegglen, S., Neuhaus, F. & Wilson, K. (2022). Voices from the Digital Classroom: 25 Interviews about Teaching and Learning in the Face of a Global Pandemic. University of Calgary Press. https://press.ucalgary.ca/books/9781773852782/ <br> TALON (2022). Online Education Card Deck. Available at: https://taloncloud.ca/TLN-Card-Deck <br> Twitter: @talon_cloud <br> Licence CC BY-NC-SA 4.0
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.584 | 0.269 |
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".