MétaCan
Menu
Back to cohort
Record W2600607963 · doi:10.56105/cjsae.v29i1.5362

Complicating Access: Digital Inequality and Adult Learning in a Public Access Computing Space

2017· article· en· W2600607963 on OpenAlexafffundvenue
Suzanne Smythe, Sherry Breshears

Bibliographic record

VenueCanadian Journal for the Study of Adult Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInternet accessThe InternetInequalityDigital divideEquity (law)Public accessUniversal designDigital literacyInternet privacyComputer sciencePublic policyPublic relationsMultimediaPolitical scienceWorld Wide WebEconomicsEconomic growthMathematics

Abstract

fetched live from OpenAlex

AbstractAccess is often defined in digital inclusion policy as the potential, if not the means, to access an Internet connection. This simple or ‘laissez-faire’ approach to digital access excludes many of the adults who are also the constituents of adult literacy education programs, suggesting the need for adult educators to attend closely to the entanglements of digital equity and adult learning. Combining methods of critical policy analysis with participant observation of adults’ experiences of digital access and learning in a public setting, the study identifies tensions between public sites for computing learning and the privatization of access, between ‘basic skills’ and critical pedagogies of production, and between the ‘model users’ for whom digital policies and the Internet are designed and the actual experiences of those who are on the margins of access. These trouble spots provoke new challenges and possibilities for a reinvigoration of public computing as new sites for adult learning.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.973
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.020
Scholarly communication0.0080.006
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.105
GPT teacher head0.431
Teacher spread0.325 · 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 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

Citations15
Published2017
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal for the Study of Adult EducationSame topicSocial Media and PoliticsFrench-language works237,207