MétaCan
Menu
Back to cohort
Record W4313450381 · doi:10.29173/isotl615

Catalyzing Conversations: Critical Thinking Skills to Win the Battle for Truth in the Post-Truth Era

2022· article· en· W4313450381 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueImagining SoTL · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsMount Royal University
FundersNatural Sciences and Engineering Research Council of CanadaMount Royal University
KeywordsJournaling file systemCritical thinkingScholarshipMisinformationCurriculumPsychologyBattleMedical educationPedagogyEngineering ethicsSociologyMedicinePolitical scienceComputer scienceEngineering

Abstract

fetched live from OpenAlex

The Scholarship of Teaching and Learning is uniquely poised to address one of the greatest challenges in the “post-truth” era through catalyzing conversations that promote the effective development of critical thinking skills necessary for identifying and avoiding conspiracy theories. An interdisciplinary team of scientists, science communicators, public health nurses and educators has designed case studies, modules and activities that are curriculum-based for use in kindergarten to grade 12 classes to promote vaccine safety. Two serendipitous outcomes from this Building Resistance to Vaccine Misinformation program included: i) significant learning experiences for everyone in our team about the other disciplines, and ii) that the research assistants articulated their own emerging professional identities. Once this program receives ethics approval, we will work with education programs to beta-test the case studies, modules and activities then assess the impacts of this program through pre and post experience questionnaires and journaling.

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.

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)0.003
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.882
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.361
Teacher spread0.339 · 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