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Record W2949527592 · doi:10.1017/s0032247419000123

Green Edge Outreach Project: A large-scale public and educational initiative

2019· article· en· W2949527592 on OpenAlex
Julie Sansoulet, Jean-Jacques Pangrazi, Noé Sardet, Sharif Mirshak, Ghassan N. Fayad, Pascaline Bourgain, Marcel Babin

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.

Bibliographic record

VenuePolar Record · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversité Laval
FundersAgence Nationale de la Recherche
KeywordsOutreachExhibitionArcticSocial mediaInfographicPublic relationsScience communicationScience educationThe arcticCitizen sciencePolitical scienceSociologyLibrary scienceGeographyWorld Wide WebComputer scienceEcologyPedagogyOceanography

Abstract

fetched live from OpenAlex

Abstract A collective outreach approach is fundamental for a scientific project. The Green Edge Project studied the impact of climate change on the dynamics of phytoplankton and their role in the Arctic Ocean, including the impact on human populations. We involved scientists and target audiences to ensure that the communications strategy was in agreement with scientists and audience requirements. We developed websites (academic site and blogs and an educational platform). Then, we produced a 52-minute documentary, ‘Arctic Bloom’, and infographics were created to explain experiments on the ice. We also organised a photo exhibition and live videos that enabled primary school-age students to ask questions directly of scientists working on the research icebreaker. Finally, both students and professionals drew their own conception of Arctic science, and our social media sites reached diverse groups of people. The evaluation results showed a large number of education structures (approximately 8000 schools and 104 museums or educational organisations) engaged with our communications outputs and encouraging statistics about website visits (117 021 and 3739 visits on the blog and the YouTube channel, respectively). Selecting different, but intersecting techniques, to promote a better understanding of the science contributed to the success of the communication and outreach outputs of the 3-year project.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.333
GPT teacher head0.429
Teacher spread0.096 · 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