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Record W2884540123

Language and Literature Educators for Fostering Media Literacy in the Nigerian Society

2015· article· en· W2884540123 on OpenAlexaboutno aff
Maureen C. Igwe, Odunola Adefunke Adebayo, Nnamdi C. Ndupu

Bibliographic record

VenueJournal of Language and Communication · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedia literacyCurriculumLiteracyPublic relationsDisseminationDigital mediaSet (abstract data type)Political scienceSociologyPedagogyComputer science
DOInot available

Abstract

fetched live from OpenAlex

Our society exists in media-saturated environment that cut across print, broadcast, digital and even online platforms, with their various missions and messages. Majority of these media outlets disseminate contents that are not in tandem with our culture, norms, values and acceptable ways of life, thereby exposing our children, adolescents and young adults unnecessarily to unacceptable messages. This necessitates the study of media and media literacy education as avenue to checkmate this rapidly evolving situation. Media literacy connote a set of skills and competencies for accessing, analysing, evaluating, creating, interpreting and understanding the complex messages that are communicated via various media outlets. It occupies a significant portion of the curriculum content of schools in the USA, UK, Australia and Canada, but is lacking in Nigeria. Thus, media studies is not yet a subject in Nigeria, and because use and communication in English depend on content of published media resources, both print and digital, there is therefore the feasibility of teachers and educators in language and literature to project media literacy instructions in the country. These are examined in the paper, with analysis of media literacy and the way forward for Nigeria.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.037
GPT teacher head0.304
Teacher spread0.267 · 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.

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
Published2015
Admission routes1
Has abstractyes

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