MétaCan
Menu
Back to cohort
Record W1972377541 · doi:10.1080/17439884.2013.783597

Digital literacy and informal learning environments: an introduction

2013· article· en· W1972377541 on OpenAlexaff
Eric M. Meyers, Ingrid Erickson, Ruth V. Small

Bibliographic record

VenueLearning Media and Technology · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLiteracyDigital literacyReuseSocial mediaSociologyPublic relationsDigital mediaInformation literacyInformal learningInternet privacyKnowledge managementEngineering ethicsPedagogyComputer sciencePsychologyWorld Wide WebEngineeringPolitical science

Abstract

fetched live from OpenAlex

New technologies and developments in media are transforming the way that individuals, groups and societies communicate, learn, work and govern. This new socio-technical reality requires participants to possess not only skills and abilities related to the use of technological tools, but also knowledge regarding the norms and practices of appropriate usage. To be ‘digitally literate’ in this way encompasses issues of cognitive authority, safety and privacy, creative, ethical, and responsible use and reuse of digital media, among other topics. A lack of digital literacy increasingly implicates one's full potential of being a competent student, an empowered employee or an engaged citizen. Digital literacy is often considered a school-based competency, but it is introduced and developed in informal learning contexts such as libraries, museums, social groups, affinity spaces online, not to mention the home environment. This article recognizes and connects the ways and places we might conceptualize and realize an expanded view of digital literacy that fits today's changing reality.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0030.008
Scholarly communication0.0070.010
Open science0.0010.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0090.001

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 head0.005
GPT teacher head0.189
Teacher spread0.184 · 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 designNot applicable
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

Citations504
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueLearning Media and TechnologySame topicLiteracy, Media, and EducationFrench-language works237,207