Does colour affect the quality or quantity of children’s stories elicited by pictures?
Bibliographic record
Abstract
The current study investigated the effect of colour vs. black-and-white pictures on the stories children told using the pictures as stimuli. Participants were 22 preschool children aged 4—6 (M = 59.98, SD = 7.52) attending day-care centres in a Western Canadian city. Two story sets of five pictures each, depicting stories with similar structure, were used as stimuli. Two versions of each story were made, one in colour and one in black and white. Each child was presented with one of the stories in colour and the other in black and white; versions and stories were counterbalanced across children. Stories were analysed for differences in content using story grammar, in amount using total number of words used in telling the story, and in vocabulary variety using number of different words used. Children were also asked which of the two stories they had preferred and why they preferred that story. Results indicated that stories children told did not differ on any of the variables; children told stories that were similar in content, length, and word variety regardless of whether the pictures used to elicit stories were in colour or black and white. When asked which story they preferred, roughly equal numbers of children expressed a preference for each version; when asked why that story was preferred, only four children ascribed it to colour, while the majority of children gave content-related reasons for their preference. We conclude that colour or lack thereof in picture stimuli does not appear to affect stories told by preschool children who are typically developing.
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 | 0.002 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".