Understanding Complex Relationships between Teachers and Parents
Bibliographic record
Abstract
Collaborative relationships between school and families are increasingly put forward as a means to promote student success and persistence. This collaborative work sometimes creates tensions and misunderstandings (Ravn, 2005) that can exacerbate divisions of power and reproduce inequalities (e.g., Crozier, 2000; Lareau, 2011; Vincent 2000). The main purpose of this study is to identify the areas of tensions and inner contradictions that emerge in the teacher-parent relationships in order to guide them while engaging in individual and collective transformation processes. Relying on Hoover-Dempsey et al.’s proposed model (2010), we conducted in-depth interviews with volunteered elementary and secondary teachers. Salient findings are discussed in light of the cultural-historical activity theory (CHAT) (Engeström, 2015). These include mainly issues of trust versus control when it comes to choosing communication tools and issues related to the hierarchical status and power inside and outside the school when it comes to the redistribution of actions among the school community members. As a promising research and intervention avenue, the authors suggest to apply the Change Laboratory method (Virkkunen & Newnham, 2013) anchored in CHAT and that uses mirror data to reflect upon and foster transformative processes, hence, relationships between teachers and parents.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".