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Record W4321996415 · doi:10.5194/egusphere-egu23-10307

Science Communication: Deeper engagement with industry and government stakeholders through documentary film, art, and graphical newsletters

2023· preprint· en· W4321996415 on OpenAlexaffabout
Chelsie Hall, David Risk

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsAnimationGovernment (linguistics)Context (archaeology)General partnershipPresentation (obstetrics)StakeholderVocabularyVisual literacyPlan (archaeology)Visual communicationVisual artsVariety (cybernetics)Public relationsPolitical scienceSociologyComputer scienceArtPedagogy

Abstract

fetched live from OpenAlex

<p>Canada’s academic science funding system requires substantial collaboration and partnership with stakeholders in government and industry. Meaningful and robust science, whether it is applied or fundamental, often depends on the ability to clearly communicate the nature and benefits of research. But the vocabulary of academic scientists and researchers usually differs from those in government and industry. Visual art and media, including artistic documentaries, creative writing, and animation, has potential to illustrate commonalities among stakeholder groups, and reach new audiences. Our research group, FluxLab, has generated effective visual media using a variety of strategies, including an informal artist-in-residence program, photography training for graduate students, and by using a graphic designer for print and web materials. In this presentation we share films, drawings, animations, newsletter layouts, and other materials used in stakeholder communication, along with context, aims, and success of each initiative.</p>

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score1.000

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 studies0.0010.003
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.554
GPT teacher head0.439
Teacher spread0.115 · 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.

Study designQualitative
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
Published2023
Admission routes2
Has abstractyes

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