Bibliographic record
Abstract
1. Beginnings John Macalister & Seyed Hadi Mirvahedi Part I: Challenges in Family Language Policy 2. Family Language Policy: New Directions Cassie Smith-Christmas 3. Family Language Policy for Deaf Children and the Vitality of New Zealand Sign Language Rachel McKee & Kirsten Smiler 4. Family Language Practices as Emergent Policies in Child-Headed Households in Rural Uganda Maureen Kendrick & Elizabeth Namazzi 5. Exploring Family Language policies Among Azerbaijani-Speaking Families in the City of Tabriz, Iran Seyed Hadi Mirvahedi 6. The Role of the Zapotec Language from Lozoga' in the Californian Migrant Community Daisy Bernal Lorenzo 7. Adrift in an Anglophone World: Refugee Families' Language Policy Challenges Diego Navarro & John Macalister PART II: Opportunities in Family Language Policy 8. How Religious Ideologies and Practices Impact on Family Language Policy - Ethiopians in Wellington Melanie Revis 9. I speak all of the language!: Engaging in Family Language Policy Research with Multilingual Children in Montreal Alison Crump 10. Dynamic Family Language Policy: Heritage Language Socialization and Strategic Accommodation in the Home Corinne Seals 11. Language Ideologies, Social Capital, and Interaction Strategies: AN ethnogrpah8ic Case Study of Family Language Policy in Singapore Guangwei Hu & Li Ren PART III: Consequences for Family Language Policy 12. Home: A Confluence of Discourses in Multilingual Linguistic Ecologies Seyed Hadi Mirvahedi & John Macalister
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".