The literacy myth continues: adapting Graff’s thesis to contemporary policy discourses on adult ‘foundation skills’ in Australia
Bibliographic record
Abstract
Harvey Graff in his 1979 study of literacy taught in common schools in mid-nineteenth century Canada, demonstrated that beliefs in the acquisition of literacy for upward mobility and economic success were a myth. Moreover, literacy instruction was promoted by educational reformers and manufacturers as a means of controlling the working class masses and instilling in them the traits, including thrift, order, and punctuality required for employment in factories. In this paper, we consider how this thesis can be adapted to describe contemporary national adult literacy policy discourse in Australia. The main drivers of Australia’s national policy are peak industry associations and skills agencies, and the human capital rationale for their promotion of literacy is derived largely from the powerful influence of the Organisation for Economic Cooperation and Development (OECD). We critique this discourse on literacy through reference to studies which conceptualise literacy as social practices, including one recent Australian study of three manufacturing companies. We reinforce the claim that the literacy myth in relation to economic development continues in contemporary adult literacy policy, and we explain how the social control function of adult literacy education continues in the interests of industry elites and the capitalist relations of production.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.044 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".