Teaching Knowledge Synthesis Methods through Online Research Consultations
Bibliographic record
Abstract
Requests to meet with academic librarians for support on knowledge synthesis (KS) projects have escalated due to an increasing number of learners embarking on KS projects as part of their course work, along with the recommendation in KS methods guidance to consult with a research librarian to ensure a comprehensive search. While there are program descriptions and evaluations of library-led and other KS methods training for groups or self-directed learning opportunities, little evidence examines the teaching practices of academic librarians in individual KS research consultations. The objective of this research is to explore teaching encounters during online KS research consultations and describe the often invisible aspects of that labour through the findings from an online-mediated, focussed ethnographic study. The study draws on data from focus groups, observations and interviews, as well as autoethnographic sources. We use a sociomaterial lens to analyze the stories in the data and illuminate the complexities of the virtual, synchronous teaching encounter between academic health librarians and learners. We present a composite narrative elaborating on the social, technical, and material elements assembled before, during, and after an online KS methods consultation to emphasize the invisible and affective labour of librarian teaching practices about comprehensive searching and KS methods.
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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 | 0.045 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".