Language and Literature Educators for Fostering Media Literacy in the Nigerian Society
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".