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Record W2973196958 · doi:10.1128/jmbe.v20i2.1759

Promoting Science Communication with Children’s Literature as a High-Impact Practice (HIP) Assessment

2019· article· en· W2973196958 on OpenAlexaff
Sarah E. Ruffell, Tommy Mayberry

Bibliographic record

VenueJournal of Microbiology and Biology Education · 2019
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsOutreachConstructiveScience communicationRelevance (law)ConversationComputer scienceWork (physics)Appreciative inquiryMultimediaScience educationMathematics educationPedagogyPsychologyEngineeringProcess (computing)Political science

Abstract

fetched live from OpenAlex

To emphasize the importance of public outreach and science communication within STEM, and to foster in students a greater appreciative understanding of the scientific content within their courses, the Science Library Project has students creating children’s books about key course content. At the end of this project, the student writer-publishers are able to display their critical and creative work as they collaborate with local teachers in a networked conversation about science and multimodal communication. This innovative pedagogical approach to assessment is important both inside and outside of the Sciences because it participates in High Impact Practice (HIP) pedagogy to have students invest a significant amount of time and effort over an extended period of time as they participate in frequent, timely, and constructive feedback and, most importantly, have the opportunity to discover the relevance of their learning through real-world applications in the public demonstration of their book projects. This practical paper shares our approach to creating and implementing the Science Library Project that activates written and visual communication modes to motivate Science learners to engage with course concepts in deeper and creative ways.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.422
Teacher spread0.403 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2019
Admission routes1
Has abstractyes

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