Child-directed language – and how it informs the documentation and description of the adult language
Bibliographic record
Abstract
Language documentation efforts are most often concerned with the adult language and usually do not include the language used by and with children. Essential parts of the natural linguistic behaviour of communities thus remain undocumented, and a growing body of literature explores what language documentation,language maintenance, and language revitalization have to gain by including child language and child-directed language. This paper adds a methodological perspective to the discussion, arguing that child language and child-directed language constitute data types that can inform our understanding of the adult language. For reasons of feasibility, the paper focuses on child-directed language only. Presenting data from two on-going language acquisition projects (Qaqet from Papua New Guinea and Dëne Sųłıné from Canada), we illustrate how this data type provides insights into the metalinguistic knowledge of adult speakers. After an introduction to child-directed language, three case studies on the topics of variation sets, clarification processes, and discourse context are exemplified from both languages and related to our understanding ofthe adult language. Focusing on the potential of this data type, this paper argues in favour of extending our documentation efforts to events involving children.
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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".