Investigating infant directed speech in a young mother sample
Bibliographic record
Abstract
How a mother linguistically interacts with her infant has lasting consequences for the infant's language development (Chase-Lansdale & Brooks-Gunn, 1994; Owen-Jones et al., 2013; Sarsour et al., 2011). There has been insufficient research describing the language environments of children born to young mothers, a term used here to describe both adolescent (18 years old and younger) and emerging adulthood (19-25 years old) mothers. No previous research has looked at maternal age and a specific type of speech, found to be of high importance in the language development process, infant-directed speech (IDS). The goal of this dissertation was to explore the type and quantity of speech infants of young mothers are exposed to, focusing on maternal IDS. I explored these variables by developing a unique labelling system (named ConvoLabel) to aid in the identification of maternal IDS in naturalistic infant language environment recordings completed by young Winnipeg mothers. The sample was comprised of 23 mothers (15 - 25 years of age), and their children (1 - 23 months of age). The Language ENvironment Analysis system, a digital recorder and software system, was used for naturalistic recording and analysis of infants’ everyday experiences (totaling over 600 hours). I found that the young mothers in my sample were using IDS both acoustically, and numerically, in a manner that is like non-young mother populations (of a western context) reported in the literature (e.g., Bergelson et al., 2019; Bunce et al., 2020; McClay et al., 2022). The infants in my sample heard more maternal non-directive IDS than directive IDS, the IDS speech type hypothesized to be more favourable for language learning and infant engagement and responsiveness (Lacroix et al., 2002; McDonald & Pien, 1982; Pratt et al., 1992). The infants were found to hear a significant amount of speech not directed to them which likely plays a role in their language learning process. This research project adds to the limited research exploring how young mothers are talking to their infants, while advancing methodology on the examination of IDS using a unique computer system.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".