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Record W2989750692 · doi:10.23860/jmle-2019-11-3-8

MEDIACY: A way to enrich media literacy

2019· article· en· W2989750692 on OpenAlex
Eva Berger, Robert K. Logan, Anat Ringel, Andrey Miroshnichenko

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

VenueJournal of Media Literacy Education · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsYork UniversityUniversity of Toronto
Fundersnot available
KeywordsMedia literacyVariety (cybernetics)Action (physics)Key (lock)Function (biology)LiteracyComputer scienceSociologyMultimediaMedia studiesPedagogyArtificial intelligence

Abstract

fetched live from OpenAlex

We propose that the discipline or practice of media literacy defined as the ability to access, analyze, evaluate and create media in a variety of forms can be enriched and made more effective by incorporating two of Marshall McLuhan’s insights into the nature of media. The first insight is that the effects of media that are independent of their content and intended function are subliminal and they are important because they “shape and control the scale and form of human association and action.” The second insight is that the notion of media includes not just communication media but also all forms of human technology, tools and artifacts. We define “mediacy” as the study, understanding and consideration of these two key insights from McLuhan, and that mediacy compliments, and enriches, the traditional media literacy approach.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.013
GPT teacher head0.275
Teacher spread0.261 · 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