Today’s Webinar: What to Expect
Bibliographic record
Abstract
Before we get started… • If you are seeing this screen, the audio portion of today’s presentation has begun. • The GoToWebinar service offers two methods of listening to today’s presentation: by computer speaker or by telephone. • If you are attempting to listen by computer speaker, please make sure the speakers are turned on. • If you’d like to listen by telephone, select Use Telephone from the module on your screen and enter in the information provided in your registration confirmation e-mail. “Although many of my friends and colleagues in the U.S. scoffed at my plans to spend a semester in Canada, since they assumed that Canada was hardly “exotic ” as other Fulbright destinations and merely an extension of the U.S., I am now in a position to stress that the Fulbright Canada program offers a wonderful opportunity to compare differences and similarities between nations that share a border. Precisely because of that shared border and influences in both directions, it is so important for U.S. scholars to understand more fully Canadian history, society, and culture, and to incorporate more material about Canada in their classrooms and research.”
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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.107 | 0.122 |
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".