A comparative study of child-directed language across five cultures based on data from the <i>Acquisition Sketch Project</i>
Bibliographic record
Abstract
Throughout the history of child language acquisition research, the study of child-directed language (CDL) has attracted significant attention. In particular, there has been considerable debate regarding the characteristic features of CDL and their universality/variability across the world’s languages. Yet, although data from many languages have been analyzed, the totality of the crosslinguistic coverage is still poor. In this paper, we report on an analysis of CDL across five diverse languages and cultures: Murrinhpatha (Southern Daly, non-Pama-Nyungan), Pitjantjatjara (Pama-Nyungan), Qaqet (Baining), Tagalog (Western Austronesian), and Inuktitut (Inuit-Yupik-Unangan). Using data collected for the Acquisition Sketch Project, an initiative in which Barb was a core member, we find both striking commonalities and clear differences in CDL across our target languages. The findings are consistent with the argument that CDL emerges as a set of culturally mediated behavioural practices, with some features being more commonly observed than others. The findings underline the value of the Acquisition Sketch approach in widening the evidence base of the field of child language acquisition, one of Barb’s major contributions to the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".