MétaCan
Menu
Back to cohort

New media literacies: At the intersection of technical, cultural, and discursive knowledges

2009· book-chapter· en· W1530655953 on OpenAlexaff
Philip Graham, Abby Goodrum

Bibliographic record

VenueOxford University Press eBooks · 2009
Typebook-chapter
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsField (mathematics)Reading (process)LiteracyCurriculumSociologyNew mediaMedia literacyPoliticsIntersection (aeronautics)Media studiesPolitical sciencePedagogyEngineering

Abstract

fetched live from OpenAlex

Abstract As a field of study, media literacy emerged along with the study of radio propaganda in the 1930s. More recently it became a field of research that has responded to the television saturated consumer cultures of the late-1960s onwards. Unlike literacies of pre-electronic media environments, those that have been studied within electronic environments have been almost solely concerned with analytical ways of reading multimedia texts. In contrast, literacies in the written word have typically involved the production of written texts as integral to curricula. The new media environment provides opportunities and challenges for research in new media literacies, not the least of which is understanding what it means for people to have a widespread potential to write themselves into global, multimediated conversations. This not only involves technical, cultural, discursive, and aesthetic knowledges, it also involves the need to be politically and economically literate in the implications of a dispersed, participatively produced, multimedia environment as distinct from the ‘broadcast’ literacies of past media environments. This article situates new media literacies in an historical framework, emphasizing the close connections among technology, culture, discourse, and related changes in political economic structures.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.920
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.042
GPT teacher head0.228
Teacher spread0.186 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations4
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueOxford University Press eBooksSame topicLiteracy, Media, and EducationFrench-language works237,207