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Record W7066752923

Jeunesse Ivoirienne et téléphone mobile dans les milieux éducatifs: de l'éducation aux médias à l'éducation à la responsabilisation

2020· other· en· W7066752923 on OpenAlexaboutno aff

Bibliographic record

VenueSocial Science Open Access Repository (GESIS – Leibniz Institute for the Social Sciences) · 2020
Typeother
Languageen
FieldDecision Sciences
TopicScientific Measurement and Uncertainty Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Mobile phoneSubject (documents)Media literacyHigher educationOrder (exchange)
DOInot available

Abstract

fetched live from OpenAlex

The mobile phone regulates the lives of many young people in Côte d'Ivoire. Its frequent use is the source of many conflicts in schools and universities in the country. The Numerous researches on the subject have concluded that media education is being introduced to the populations, particularly the "digital natives," in order to awaken in them its critical spirit vis-à-vis the media. With a view to identifying the impact of media education on young people, during the first quarter of the 2019-2020 school year, we conducted a survey of 100 students and 80 students, 12 teachers and administrative staff and 08 parents of students in selected secondary, technical and higher education institutions in the municipality of Yopougon. The \nresults of this field study shed light on the limitations of media literacy today as the only solution to the many conflicts of its use in schools.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.218

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
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 head0.554
GPT teacher head0.575
Teacher spread0.020 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2020
Admission routes1
Has abstractyes

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