The Chisasibi Child Language Acquisition Study (CCLAS): A Progress Report
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Chisasibi Child Language Acquisition Study (CCLAS) is a longitudinal naturalistic first language (LI) acquisition study of Northern East Cree-speaking children located in the community of Chisasibi, Quebec (on the east coast of James Bay).1 The study focusses on production only. Our aim in this paper is to provide an overview of the progress we have made in this study, which has n o w entered its third year, and to provide some sense of the challenges encountered and the solutions w e came up with in response. W e describe the procedures developed, in many cases through trial and error, in order to create an efficient way to conduct a longitudinal acquisition study, where researchers are not actually on site for much of the time. Our aim in this paper is to provide a how-to guide for other researchers or communities wishing to undertake comparable research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it