Bibliographic record
Abstract
Causatives have received considerable attention in first language acquisition. Of Mayan languages, acquisition of the causative has only been investigated for K’iche’ and Tzotzil, based on longitudinal and spontaneous data. K’iche’-speaking children do not acquire morphological causatives until the age of 3 years, while children acquiring Tzotzil start producing morphological causatives around the age of 2 years. The marked difference in the age of acquisition of causatives in K’iche’ and in Tzotzil has been explained through a morphological difference between causatives in the two languages. This paper, based on longitudinal and spontaneous data, examines acquisition of the causative in Q’anjob’al, a third Mayan language. The question is whether the findings in K’iche’ and Tzotzil are reproduced, or whether the acquisition of Q’anjob’al causatives follows a third, yet-unattested, trajectory. The results show that three Q’anjob’al children, of the age range 1;9-3;0, 2;3-4;0, and 2;7-3;6, acquire the morphological and periphrastic causatives during the third year of life, although these children produce more periphrastic causatives than morphological causatives. Longitudinal and spontaneous studies in K’iche’ and Tzotzil have reported the acquisition of the morphological causative, but not the acquisition of periphrastic causatives. The Q’anjob’al children’s production of more periphrastic causatives might be due to their exposure to a special V1V2 construction, which is a general feature of Q’anjob’al. The Q’anjob’al child data show that even related languages in which causatives are expressed through similar morphemes can show strikingly different acquisition trajectories.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".