Bibliographic record
Abstract
I organized and moderated the session Technical Services Roundtable at the 2011 Canadian Library Association Conference in Halifax. The session was Friday, May 27 8:30-10:00 am. 41 people attended the session. We had 5 small groups that discussed 3 of the 7 questions that were provided. Much of the discussion was around RDA (the new cataloging rules), how the library catalog can/should work in conjunction with other discovery tools for obtaining catalog records, preparing for RDA implementation, general role of technical services departments given the many types of materials that are used by library patrons, and collaboration among libraries re catalogue records. There was lively, animated discussion. Attendees commented that they greatly appreciated the opportunity to share challenges with people working in other libraries and in different types of libraries—academic, government, school libraries. I found the opportunity to share experiences with others working in the same area to be invaluable. Hopefully, this type of session will continue to be offered at the conferences in the future. The rest of the conference was excellent. Sessions that I particularly enjoyed included ones about RDA, how to present library statistics information, copyright as applied to libraries, and information on the implementation of access to various mobile interfaces. The trade show was a great opportunity to learn about new initiatives and plans for the future from various vendors.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.001 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.621 | 0.483 |
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".