Homeschooling and Publicly Funded Education - Achieving the Best of Both Worlds
Bibliographic record
Abstract
This Dissertation-in-Practice (DiP) provides a roadmap to address the trend of increased numbers of parents choosing homeschooling in the Central Catholic School Board (CCSB, a pseudonym). The CCSB is a publicly funded Ontario Catholic school district serving more than 40,000 students across a mixed urban, rural, and suburban region. A critical theory approach to addressing the increase in homeschooling by students from underserved marginalized communities provides an explicit focus on social justice. The selected solution to address the complex problem is a system-wide implementation of flexi-schooling. A future state sees flexi-schooling providing alternatives to status quo structures by allowing personalized learning pathways that include some instruction at home and some instruction at school. The DiP addresses the importance of the director of education and executive council leading the change initiative. Transformative and third-order change may result in flexible attendance options, independent course selection, optional participation in assessment and evaluation, and personalized graduation pathways. Strategies for system-wide change include leveraging the CCSB’s existing focus on deep learning and use of a coherence framework. The DiP explores both transformative and distributed leadership practices during the change implementation process. It also examines a modified change path model along with a monitoring, evaluation, and communication plans. The change implementation plan spans three-years to coincide with the district’s multi-year strategic plan. The roadmap to system change has the potential to disrupt the status quo structure of publicly funded education.
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 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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".