The Inclusion of Parents and Families in Schooling: Challenging the Beliefs and Assumptions that Lead to the Exclusion of Our Students’ First Teachers
Bibliographic record
Abstract
Through autobiographical narrative inquiry, I use my stories of experience to unpack the notion of inclusion, and then, using that definition, I explore commonly held beliefs and assumptions about the positioning of parents and families in schools. I discuss how educators often unintentionally exclude parents and families from their children’s schooling through common, taken-for-granted institutional practices, and how these practices then continue to perpetuate exclusion. I share my rethinking of practices, and I extend Pushor’s notion of parent engagement, reframing her conceptualization through a lens of inclusion and ethical space. I explore how educators might work with parent voice and presence in an inclusive way, repositioning ourselves alongside parents and engaging in authentic relationships to deepen learning opportunities for our students and enriching the lives of our students and their families, as well as our own.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.051 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.009 |
| 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".