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Record W2520699618 · doi:10.1002/lob.10151

A Shifting Tide: Recommendations for Incorporating Science Communication into Graduate Training

2016· article· en· W2520699618 on OpenAlexaff
Elizabeth J. Hundey, Jennifer H. Olker, Cátia Carreira, Rémi Daigle, Ashley K. Elgin, Michael Finiguerra, Natasha J. Gownaris, Nicole Hayes, Leanna Heffner, N. Roxanna Razavi, Patrick D. Shirey, Bradley B. Tolar, Elisha M. Wood‐Charlson

Bibliographic record

VenueLimnology and Oceanography Bulletin · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversité LavalWestern University
FundersNational Science Foundation
KeywordsTraining (meteorology)Medical educationScience communicationPsychologyGraduate studentsPublic relationsPolitical scienceScience educationPedagogyMedicine

Abstract

fetched live from OpenAlex

Abstract Scientists who are skilled in communication reap professional and personal rewards. Unfortunately, gaps exist in fostering curricular and extracurricular training in science communication. We focus our article on opportunities for university‐ and department‐level leadership to train new scientists to communicate effectively. Our motivation is threefold: (1) communication training is key to being competitive in the increasingly diverse job market, (2) training early career scientists in communication “jump‐starts” personal and societal benefits, and (3) the authors represent a group of early career aquatic scientists with unique insights on the state of and need for training. We surveyed early career aquatic scientists about their science communication training experiences. In summary, survey respondents indicated that (1) science communication training is important; (2) graduate students are interested in training that is not currently available to them; (3) departments and advisors are moderately supportive of students participating in science communication, but less enthusiastic about providing training support; and (4) graduate students lack opportunities to put science communication training into practice. We recommend departments and institutions recognize the benefits of science communication training, develop a strategy to support such training, and facilitate individualized approaches to science communication.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.778
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.454
GPT teacher head0.441
Teacher spread0.013 · 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 designNot applicable
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

Citations26
Published2016
Admission routes1
Has abstractyes

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