Pseudotemporal Invitations: 6- to 9-year-Old Maltreated Children’s Tendency to Misinterpret Invitations Referencing “Time” as Solely Requesting Conventional Temporal Information
Bibliographic record
Abstract
Forensic interviewers ask children broad input-free recall questions about individual episodes in order to elicit complete narratives, often asking about "the first time," "the last time," and "one time." An overlooked problem is that the word "time" is potentially ambiguous, referring both to a particular episode and to conventional temporal information. We examined 191 6-9-year-old maltreated children's responses to questions about recent events varying the wording of the invitations, either asking children to "tell me about" or "tell me what happened" one time/the first time/the last time the child experienced recent recurrent events. Additionally, half of the children were asked a series of "when" questions about recurrent events before the invitations. Children were several times more likely to provide exclusively conventional temporal information to "tell me about" invitations compared to "tell me what happened" invitations, and asking "when" questions before the invitations increased children's tendency to give exclusively conventional temporal information. Children who answered a higher proportion of "when" questions with conventional temporal information were also more likely to do so in response to the invitations. The results suggest that children may often fail to provide narrative information because they misinterpret invitations using the word "time."
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".