Parental involvement in children's learning: Mothers' fourth shift, social class, and the growth of state intervention in family life
Bibliographic record
Abstract
Nation states across the global North are restructuring their education systems. This process has changed the relationship between school and home, with an increasing onus being placed on parents to involve themselves in their children's education. The article explores what mothers with different social class positions think about state attempts to enrol them in the education of their primary‐aged children (ages 4–11), and considers their experience of school curriculum events designed to encourage and guide their help for children's learning within the home. Mothers’ support for this form of educational restructuring is widespread, but motivations for, and experiences of, involvement vary significantly between higher‐, middle‐ and low‐income schools. This matters as parental involvement not only increases mothers’ workloads—adding a fourth shift to the existing demands of paid labour, domestic work, and their own education/training—but also risks widening social inequality as middle‐class children potentially benefit more than their working class counterparts. In conclusion, the article emphasizes the need for geographies of education to: explore parents’ gendered and classed engagement with education; trace the sectors’ changing spatiality in the context of growing links between different sites of learning; and produce geographies that look both inward into the education system and outward at its importance in wider society.
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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 | 0.002 | 0.003 |
| 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 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".