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Record W4389944697 · doi:10.33137/cjal-rcbu.v9.40999

Teaching Knowledge Synthesis Methods through Online Research Consultations

2023· article· en· W4389944697 on OpenAlexaffvenue
Robin Parker, Erna Snelgrove‐Clarke

Bibliographic record

VenueCanadian Journal of Academic Librarianship · 2023
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsQueen's UniversityDalhousie University
Fundersnot available
KeywordsNarrativeEthnographyAutoethnographyFocus groupMedical educationPsychologyPedagogyWork (physics)SociologyMedicineEngineeringSocial science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.008
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.421
GPT teacher head0.558
Teacher spread0.137 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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