Library technicians collaborating with librarians on knowledge syntheses: a survey of current perspectives
Bibliographic record
Abstract
Introduction: Though it is well recognized that librarians bring value to knowledge synthesis teams, library technicians have largely been excluded from this process. This study was designed to determine the extent to which library technicians are currently participating in knowledge syntheses, to investigate where these two professional groups, librarians and library technicians, see opportunities for future collaboration, and to identify the challenges and successes perceived by both groups. Methods: An electronic survey, consisting of multiple choice and short answer queries, was distributed to targeted listservs. The target audience for survey participants was librarians, or library technicians, who worked in a library with any scale of knowledge synthesis service. Responses were collated, coded, and organized by themes. Results: 170 responses were received and evenly represented librarians (n=84) and library technicians (n=79), including 7 incomplete responses. 31% (n=50) of respondents stated that they currently collaborate or have collaborated in the past on knowledge synthesis projects with the other professional group. Tasks completed by the library technician included article retrieval, citation management, retrieving reference lists, and database searching. The major challenge reported with collaboration on knowledge synthesis projects was the library technician qualifications. Major successes included time efficiency for librarians, and the opportunity for technicians to develop new skills.
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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 | 0.095 | 0.191 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".