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Record W1991688016 · doi:10.1177/1468798411401863

‘From bricks to clicks’: Hybrid commercial spaces in the landscape of early literacy and learning

2011· article· en· W1991688016 on OpenAlexfundno aff
Helen Nixon

Bibliographic record

VenueJournal of Early Childhood Literacy · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
FundersAustralian Research CouncilBrock UniversityUniversity of South Australia
KeywordsLiteracySociologyPedagogyIdeologyPoliticsPolitical science

Abstract

fetched live from OpenAlex

In their quest for resources to support children’s early literacy learning and development, parents encounter and traverse different spaces in which discourses and artifacts are produced and circulated. This paper uses conceptual tools from the field of geosemiotics to examine some commercial spaces designed for parents and children that foreground preschool learning and development. Drawing on data generated in a wider study, I discuss some of the ways in which the material and virtual commercial spaces of a transnational shopping mall company and an educational toy company operate as sites of encounter between discourses and artifacts about children’s early learning and parents of preschoolers. I consider how companies connect with and ‘situate’ people as parents and customers, and then offer pathways designed for parents to follow as they attempt to meet their very young children’s learning and development needs. I argue that these pathways are both material and ideological, and that they are increasingly tending to lead parents to the online commercial spaces of the World Wide Web. I show how companies are using the online environment and hybrid offline and online spaces and flows to reinforce an image of themselves as authoritative brokers of childhood resources for parents, which is highly valuable in a policy climate that foregrounds lifelong learning and school readiness.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.371
Teacher spread0.340 · 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 designObservational
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

Citations12
Published2011
Admission routes1
Has abstractyes

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