Becoming a Home-Educator in a Networked World: Towards the Democratisation of Education Alternatives?
Bibliographic record
Abstract
The internet is assumed to play a special role in UK home-education and has apparently fuelled an increase its prevalence. This paper reports the place and purpose of the internet, online networks and offline communities in the decision to home-educate amongst parents in England, Scotland and Wales. The research formed part of a mixed-method doctoral study that included: an online survey of 242 home-educators; 52 individual and group interviews with 85 parents, children and young people and a week-long participant observation with families. The sample included a range of both ‘new’ and ‘experienced’ home-educators. The findings show that online and offline networking helped prospective parents to learn of home-education as a viable and positive alternative to schooled provision. For parents, socialising with existing home-educators was pivotal for cultivating a sense of identity, belonging and commitment to an education without school. At the same time, becoming a legitimate home-educator was a complex achievement; hinged upon social and economic resources and cultural competencies. Evidence of exclusionary practices among home-educators both online and offline, challenges the extent to which home-education is truly more ‘open’ now than it once was. In the decision to home-educate, it is concluded that the democratising potential of the internet points to ‘old wine in new bottles’.
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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 | 0.009 | 0.010 |
| 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 | 0.019 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".