“Let Me Show You a Trick!”: A Toddler's Use of Humor To Explore, Interpret, and Negotiate Her Familial Environment During a <i>Day in the Life</i>
Bibliographic record
Abstract
Children employ different types of humor as they explore, interpret, and negotiate their environments. Whereas an appreciation of verbal incongruity has been a hallmark of older preschooler humor (e.g., McGhee, 1989), more recently, other violations of expectations and clowning also have been identified as ubiquitous during the first two years of life (e.g., Loizou, 2005; Reddy, 2001). We examined the pragmatics of one 30-month-old girl's humor, and determined how it interactively harnessed the cognitive, linguistic, and socio-emotional resources available to her negotiations within her familial context. Using the methodology of a Day in the Life of a toddler in early childhood (Gillen et al., 2006), the child's entire waking day was videotaped, and all interactions were transcribed and analyzed. We identified many instances of humor, and categorized them into four major types: clowning, teasing, jokes and playful language, and physical actions. Humor served both socio-emotional and cognitive-linguistic functions, and we confirmed Reddy's (2001) finding that early humor is interpersonally co-constructed: When humor operates within the child's inter-mental development zone (Mercer, 2000), it serves to inform her or his intra-mental growth; the inter-mental precedes and enables the intra-mental.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".