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Record W2952596699 · doi:10.15173/sciential.v1i2.2126

Science Communication: An Interview with Katie Moisse

2019· article· en· W2952596699 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueSciential - McMaster Undergraduate Science Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsMcMaster University
Fundersnot available
KeywordsScience communicationMultitudeStorytellingField (mathematics)Engineering ethicsJournalismPublic relationsTechnical communicationBridge (graph theory)SociologyScience educationPolitical sciencePedagogyMedia studiesEngineeringNarrative

Abstract

fetched live from OpenAlex

Science communication is an emerging field that consists of a multitude of different career options, such as journalism, storytelling, and multimedia production. At its core, the field of science communication represents the bridge between scientists and the general public. Experts in this profession are concerned with how the complexities of scientific research can be presented to all audiences in ways that are engaging, comprehensible, and relevant. Today, innovative scientists continue to push the boundaries of knowledge and they are supported by science communicators who help to raise awareness and advocate for the research. Despite the fundamental role that these experts play, many people are unaware of the field of science communication and the vast array of career opportunities that it offers. The purpose of this interview is to shed light on science communication and to explore the associated skills, careers, and growth opportunities.

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)0.012
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0060.008
Scholarly communication0.0040.007
Open science0.0040.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.265
GPT teacher head0.431
Teacher spread0.166 · 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