Post-Modern Cataloging: It's All AV Now
Bibliographic record
Abstract
Closing Keynote speaker Lynne Howarth is the Associate Dean of Research and a professor at the Faculty of Information at the University of Toronto. Her research interests include knowledge organization standards and systems, as well as the evaluation of libraries’ technical services. Howarth’s professional memberships include the Canadian Committee on Cataloguing, the International Federation of Library Associations (IFLA) Classification and Indexing Section, and IFLA’s ISBD Review Group. The theme of the conference this year was “post-modern cataloging.” Howarth chose this theme for her presentation to reflect the dramatic shift of the media landscape in the last 20 years. She contends that media creation and management have moved from an “expert/gatekeepers” model to a “new player” model. In the first model, media is created and managed by “old guard” entities such as newspapers, television networks, and publishers. In the second model, media creation and management is more diffuse, constructed and recycled by an “everyman creative class” via social media, retail, devices, etc. The key players in this model are Google, Amazon, Facebook, Twitter, and many millions of media users worldwide. Howarth led the attendees on an amusing tour of the 2012 conference that tied the sessions presented back to the conference theme.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.027 | 0.024 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.121 | 0.042 |
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".