Digital reference services: a snapshot of the current practices in Scottish libraries
Bibliographic record
Abstract
Abstract: Purpose This paper aims to study information behaviour of academics in the digital age. Compares information behaviour of British university academics in three disciplines computer and information sciences, business/management, and English literature. Design/methodology/approach Discusses information behaviour of academics in the digital age. Findings English academics make higher use of printed information resources, such as text and reference books, than academics of any other discipline included in this study; they generally tended to be the least frequent users of electronic resources such as full-text databases, indexing and abstracting databases, search engines, and internet sites. CIS academics generally tended to make greatest use of electronic-based information resources, and the least use of print-based information resources, and business/management academics fell somewhere in between these two disciplines. CIS academics were generally the most enthusiastic about the benefits of electronic resources, whereas English academics were the least enthusiastic about them. Nearly a quarter of English academics disagreed to some extent that electronic information was easier to use than printed resources, which might go some way to explain their lower use of electronic materials, and higher use of printed materials.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".