Teams Are Now Used by Many Technical Services Departments in Academic Libraries
Bibliographic record
Abstract
Objective – An investigation of the use of teams in technical services, provision of training on team-working, characteristics of technical services teams, and the effectiveness of teams. Design – Survey comprising of 19 closed questions and one open question. Setting – Technical services departments in academic libraries. Subjects– Responses were received from 322 library staff members. Of those, 294 answered the survey question about team-based technical services and 55.9% of respondents completed the full survey. Methods – An online survey was promoted via seven technical services electronic mail lists and was conducted using SurveyMonkey. Main Results – The survey found that 39% of technical services were entirely team-based, 18% were partly team-based, and 43% did not use teams. Information was gathered about the number of teams, team nomenclature, and how long teams have been used. This research highlighted the lack of provision of training and documentation about working in teams. Conclusion – Many respondents have team-based technical services, and most participants found that working in teams had a positive impact. A systematic application of this survey is planned for the future.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".