PressReader - The changing landscape to digital transcript
Bibliographic record
Abstract
PressReader -The changing landscape to digitalLisa Hewitt: This webinar is being recorded, and will be shared with attendees within the next week.The video of this will also be available on our events web page after the event.So, you can point members of your institutions to watch it later.All attendees are on mute at the moment, that's just a standard for our Webinars.We will welcome any questions within the chat pane or in the Q. & A. pane at any time, and these might be answered, as we move along, or they will be answered in our Q.& A. session at the end of this webinar.The agenda is as follows: there is an introduction and demonstration.Fireside chat, and then the question-and-answer session.So, I would like to hand over now to our colleagues at PressReader.Oghenevwoke Shehu Usman: Thank you very much, Lisa.Thank you for, for putting this together, and very warm welcome to all attendees of the of this of this Webinar.My name is Oghenevwoke, and I work for PressReader.I thank you all for joining this this webinar.So without further ado, I'm just going to go ahead and
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.338 | 0.180 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".