The acquisition of the passive voice in Northern East Cree
Bibliographic record
Abstract
This article analyzes the acquisition of the passive voice in Northern East (NE) Cree and pays particular attention to the interaction of frequency effects and language-specific cues in the way children form and employ expectations, the process of anticipating oncoming structure in the ambient language. The passive has long been of interest in first-language acquisition, and expectations may play a role in the reported challenges acquiring the passive in languages such as English. We present two studies analyzing approximately 24 hours of naturalistic video recordings involving one adult and two children: Daisy (age 3;08–5;11) and Billy (4;06–5;10). Study 1 examines the passive voice in child-directed speech (CDS). CDS employs passive verbs frequently, at rates much higher than what has been reported for other languages. Passives also typically occur without overt arguments and most often are derived from verbs with two animate participants. Study 2 traces the acquisition of the passive by Daisy and Billy. Daisy demonstrates productivity with all three passive suffixes by age 3;11. Billy’s recordings begin at a later age, and he shows productivity with suffixes -naaniu at 4;06, -ikiwi at 4;10, and -ikiniw at 5;05. Both children produce passives at rates much higher than what has been reported in child speech for other languages. They also most frequently produce passives without overt arguments, and they show no difficulties passivizing verbs with two animate arguments. These results expand the typological purview of passives research and shed additional light on the role of expectations in acquiring the passive voice. The combination of high frequency and particular structural characteristics for the passive voice in Cree CDS allows children to build expectations differing greatly from those of children acquiring languages such as English. In turn, children acquiring NE Cree do not show the same difficulties in acquiring the passive voice.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".