‘From bricks to clicks’: Hybrid commercial spaces in the landscape of early literacy and learning
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".