Working and Learning With Families, Communities, and Schools: A Critical Case Study
Bibliographic record
Abstract
It’s 1:00 on a pleasant March afternoon. We are in the staff room of Valley School in Vancouver, British Columbia, having just concluded another session in the family literacy program called PALS. The focus of today’s session was on reading with children. The kindergarten teachers, Suzanne, the program facilitator, and I are discussing several of the issues that arose as we worked with a group of parents over the last several hours. One of the teachers comments that some of the parents seemed concerned with the selection of children’s books that we incorporated into the classroom learning centres today. Indeed, during the debriefing and follow-up discussion, one of the parents commented that her son really enjoys an old “reader” that they had purchased at a yard sale; we noted the affirmative nods. Our talk then turns to the dearth of children’s books available in languages other than English and the challenges of trying to rectify this situation in a school where more than a dozen language groups are represented. The school bell signals the beginning of the afternoon session and the teachers busily head off to their classrooms. I write a note to myself: “We must address the question from the parent about her daughter’s fascination with making signs and notices and displays and her comment that her child is not interested in storybooks, at the next session.”
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.047 | 0.019 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".