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

Science Communication: An Interview with Katie Moisse

2019· article· en· W2952596699 on OpenAlexaffvenue
Tyler Redublo

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.029
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0280.011
Scholarly communication0.0070.011
Open science0.0020.006
Research integrity0.0090.021
Insufficient payload (model declined to judge)0.0050.002

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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 routes2
Has abstractyes

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