MétaCan
Menu
Back to cohort
Record W2795852989 · doi:10.1177/0741713618766645

Adult Learning in the Control Society: Digital Era Governance, Literacies of Control, and the Work of Adult Educators

2018· article· en· W2795852989 on OpenAlexafffund
Suzanne Smythe

Bibliographic record

VenueAdult Education Quarterly · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaSimon Fraser University
KeywordsLiteracyGovernment (linguistics)SociologyCorporate governanceContext (archaeology)Digital literacyPedagogyAdult educationPublic relationsControl (management)Political scienceManagement

Abstract

fetched live from OpenAlex

This article reports on a study of adult literacy and learning in a public computing center where people contend with the new literacy demands of online government and other automated technologies. The study asks, (1) What literacy and learning practices are associated with digital governance? (2) What pedagogies support people to navigate digital government and automated technologies? (3) What are the broader implications of digital government for the work of adult educators? Bringing together sociomaterial theories of learning and methodologies of ethnographic case study, the study maps the literacies and pedagogies of digital government in the context of Deleuze’s society of control, arguing that digital-era governance spurs new forms of cognitive labor, new digital literacies and new pedagogies that are reshaping adult learning and the work of adult literacy educators. The article considers potential openings to “more than human” research and pedagogies that reconfigure adult literacy research and practice as sites of resistance to the control society.

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)0.005
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.028
Scholarly communication0.0090.007
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.004
GPT teacher head0.266
Teacher spread0.262 · 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 designTheoretical or conceptual
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

Citations29
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueAdult Education QuarterlySame topicSocial Media and PoliticsFrench-language works237,207