Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Library 3.0 is just around the corner – so what are the selection criteria for Library 3.0 workers? How do we cope with four generations amongst our workforce? Is it a willingness to explore and gain a range of new skills often in their own time? Are we spending too much time trying to work out what services our customers want and not enough time skilling up some of our staff? Are the professional associations keeping us up to date with the necessary professional training programs? If they are not then where do we go? Just as television has not superseded live theatre, how do we maintain the elements in Library 1.0 and 2.0 in the new era? While library staff are enthusiastically taking up the new challenges, the challenge is to be both custodian and facilitator of information resources. This paper will explore the issues and challenges in keeping up with the impact of new technologies and the mega trends that will occur in the next quarter of the 21 st century: how can libraries learn from other service industries, how will librarians keep up with subject specific skills e.g. evidenced based medicine, law, problem based learning, are our skills out of alignment with these trends, are we taking advantage of our potential?
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it