The Browsers Are A’Changing: Lessons from the Aftermath (?) of Browser Changes
Bibliographic record
Abstract
Some big happenings are coming soon in the world of internet browsers. Building on previous panel sessions presented at the CORE Forum, the Charleston Conference, and ER&L, this speaker's goal is to have a lively discussion about the basics of browser changes, focusing on why and what has changed, specifically over the last quarter or two. Real-world examples are both sought out and encouraged during this discussion. This speaker, a member of the American Library Association’s CORE Federated Authentication Committee, will relate what we have learned and focused on during the intervening months after more browser changes are rolled out. Focusing on NASIG-specific topics, this speaker would like to evaluate how users grapple with the changes through the NASIG Core Competencies for Electronic Resources Librarians lens. Particular attention will be given to the Lifecycle of Electronic Resources, Technology, and Effective Communication sections. This speaker aims to create a collaborative network for library personnel to rely upon for assistance in navigating browser changes.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".