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Record W3209070324 · doi:10.3389/fcomm.2021.758198

Teaching Science Communication with Comics for Postgraduate Students

2021· article· en· W3209070324 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueFrontiers in Communication · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicComics and Graphic Narratives
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsComicsContext (archaeology)StorytellingScience communicationDisciplineStudent engagementCommunication skillsMathematics educationEngineering ethicsComputer scienceScience educationSociologyPsychologyMedical educationEngineeringSocial scienceNarrativeArtMedicineGeography

Abstract

fetched live from OpenAlex

Data visualization and visual storytelling are increasingly common terms when institutions and scientists want to introduce people to their research and science through stories. Yet institutions mostly teach and train their scientists in the language of science and scientific journals, whereas research dissemination calls for other forms of communication. A new university course introducing such a new form of communication is proposed to postgraduate students at Université de Sherbrooke since January 2020. Its main objective is to help students develop their general interest and skills into science communication using comics as a working medium. While following a simple path, this course has generally led to results beyond initial expectations and large engagement from students. This study describes the general context and structure of the course, analyzes feedback from participants, presents some results, and summarizes lessons learned to help the diffusion of such a cross-disciplinary course.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.026
GPT teacher head0.285
Teacher spread0.259 · 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