Librarian Involvement in Knowledge Synthesis at the University of Manitoba
Bibliographic record
Abstract
Introduction: Librarians play a vital role in knowledge synthesis (KS) research from constructing the search strategies and completing the data collection. Depending on the level of support librarians may be given co-authorship, an acknowledgement, or an in-text mention. While building upon previous work that benchmarks KS output at an institution, this work specifically examines KS at [institution name] wherein our faculty are primary authors and if and how librarians were credited for their work. Methods: We undertook a content analysis of all KS published between 2017-2022 by University of Manitoba authors. We searched affiliation in PubMed and Scopus to retrieve 2,602 records. After screening, we had 696 records. From these records we determined the type of KS (e.g., rapid, scoping), the broad topic area (e.g., medicine, nursing, education) whether the primary authors were UM-affiliated, and the credited role of the librarian (if any).Results: We will calculate the type and number of KS completed during this period, the most popular disciplines, if the project used a librarian and if so, how the librarian was acknowledged. Results will be available in Spring 2023. DISCUSSION: The information gathered in this research will be turned into action in three ways: 1) Advocating to our administration and faculty on the importance of this work with accurate data on the true scope of KS research at our institution; 2) Identifying UM-affiliated authors who are doing KS research without the support of librarians and who may be amendable to outreach 3) Quantifying the level of credit librarians are getting for their KS work.
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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 | 0.090 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.019 | 0.046 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.007 |
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".