Co-creating hybrid discursive spaces that integrate teacher and family knowledge around children's literacy development to create meaningful relationships between home and school
Bibliographic record
Abstract
When educators lack knowledge andskills in understanding and intentionally enacting effective and sincere familyengagement practices with their diverse students' families, children's longterm life chances are compromised (Constantino, 2005; Desforges,& Abouchaar, 2003; Edwards, Pleasant & Franklin,1999; Epstein, 2009, 2011; Feiler, 2010; Harris, Andrew-Power, & Goodall, 2010; Henderson & Mapp, 2002; Jeynes, 2005; Martin,& Hagan-Burke, 2002; Pushor, 2007). Whenfamilies of children do not interact with educators, it is often viewed as lackof value for education and presumed to be indicative of disinterest and apathy (Edwards,Pleasants, & Franklin, 1999; Delpit, 2006; Lawrence-Lightfoot, 2003;Manyak, & Dantas, 2010; Pushor, 2007; Schultz, 2010). This doctoral researchstudy asks the question: How will co-creating discursive spaces in which teachers and families integratetheir respective funds of knowledge around children's literacy developmentimpact the interface relations between home and school and the lived curriculumin the classrooms? Conditions will be co-created througha critical participatory action research methodology with educators andfamilies that join together in processes that allow them to align theirrespective funds of knowledge (Moll, Amanti, Nell, & Gonzalez, 1992) regardingtheir children and their children's emerging literacy development.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".