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Record W4389094989 · doi:10.4000/asp.8611

Research dissemination in digital media: An online survey of French researchers’ practices

2023· article· en· W4389094989 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.

Bibliographic record

VenueASp · 2023
Typearticle
Languageen
FieldComputer Science
TopicE-Learning and Knowledge Management
Canadian institutionsWorkplace Health, Safety and Compensation Commission
Fundersnot available
KeywordsContext (archaeology)Digital mediaAction (physics)Public relationsWork (physics)Science communicationDisciplineSociologyPolitical scienceComputer sciencePedagogyWorld Wide WebScience educationEngineeringSocial scienceGeography

Abstract

fetched live from OpenAlex

Researchers are currently encouraged to make their results more accessible and transparent for their peers and to engage wider, lay audiences in science through new digital media. The Campus Iberus digital science action group carried out a first study to investigate how Spanish scientists communicate their work through digital media (Perez-Llantada et al. 2022). As international partners of this action group, members of the GERAS working group Literacies in Academia, Science and the Professions have replicated the online survey across several Higher Education Institutions in France. Here, we report on the results of the survey concerning both STEMM and HSS researchers. The aim is to identify the types of online science communication used by researchers in the French context, especially those targeting wider, non-specialist audiences. The survey results point to disciplinary and gender differences and allow us to analyze the needs of these researchers in terms of developing the necessary language, communication and digital skills to communicate effectively in this context.

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)0.006
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.347
GPT teacher head0.472
Teacher spread0.125 · 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