Organized a session at the 2008 Canadian Library Association Annual Conference & Trade Show in Vancouver, BC, May 21-24, 2008
Bibliographic record
Abstract
The session went well. There were 25 people in attendance. \n \nWe had 3 discussion topics: Should MARC records from e-book vendors be modified and how, \nWhat is the role of Technical Services staff in the managing of electronic resources, and How do you motivate long term employees. Each table discussed each topic for about 30 minutes. \n \nAll three topics were well received and there was good discussion about each topic. Some of the comments have been collected and will be shared on the wiki of the Canadian Library Association’s Technical Services Interest Group. \n \nEveryone liked the informal format that included the opportunity to have food and beverages while discussing topics of interest. It is hoped to repeat the session and format next year at the CLA conference in Montreal. \n \nI found the discussions interesting. There were a few suggestions relating to workflow and motivation that I will be using in the AU Library.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 teacher head, 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".