Bibliographic record
Abstract
When working at a community college in Ontario, my colleagues and I frequently interacted with students and their parents. However, limited institutional resources supported such interactions and much of our work with parents was performed off the sides of our desks. As both an administrator and academic researcher, I was conflicted by how scholarly descriptions of parent involvement differed from my professional encounters with parents of students. I was inspired to synergize the support students receive in their college journeys: both the familial and institutional. Thus, the purpose of my doctoral research became to explore the involvement of parents in their students’ education journeys at four public Ontario Colleges. I grounded my research in Bronfenbrenner's (1977) & Bronfenbrenner & Morris' (2006) bioecological model. I used the bioecological model to conceptualize influences (including parents) on human (student) development. Through website content analysis, telephone interviews, and surveys, I sought to elucidate the nature of parent involvement in the context of college in Ontario. I used the parallel-databases variant of convergent design to collect and analyze data on the involvement of parents in the lives of college students, through three distinct research strands: (i) interviews with college employees,(ii) surveys of college employees, students, and parents, and (iii) college website content analysis. My study yielded novel contributions to the literature, including that the involvement of parents impacted both students and college employees. My research documented that parents (& other family members) were involved in a myriad of ways and that their involvement both positively and negatively affected students. My findings also highlighted a range of perspectives in regards to how parents were foreseen as involved in students’ college related experiences. In particular, I found institutional and college employees’ perceptions of the home-school relationship, often conflicted with student expectations and the involvement practices of parents (especially for families from marginalized backgrounds). Overall, I concluded that, in Ontario, parents are involved throughout students’ college journeys, from recruitment through to graduation, and beyond. Ultimately, I hope that my dissertation may inform professional practices seeking to support the involvement of parents in students’ college journeys in Ontario.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".