Representations of knowledge states when writing: Keeping the reader's mind in mind.
Bibliographic record
Abstract
Representations of knowledge states when writing: Keeping the reader’s mind in mind Joan Peskin University of Toronto, OISE Carly Prusky University of Toronto, OISE Julie Comay University of Toronto, OISE Abstract: By 5 years children can represent knowledge states in Theory of Mind (ToM) tasks, yet elementary-school children rarely Think about their Reader’s Mind (ToRM) when writing. This research reduced the information- processing demands of writing by using dictations to examine awareness of a reader’s ignorance. Children aged 5 to 7 years dictated various pairs of letters (e.g., about playing in the snow to a child who had never seen snow versus a Canadian child). Results showed that only a quarter of the 5-year-olds, half the 6-year-olds, but most 7-year- olds showed evidence of ToRM. There were significant correlations between ToRM, ToM and Executive Function, however, on a measure of ratio of changes to total words, with age partialed out, correlations with ToM and Executive Function were no longer significant. Results are discussed in terms of information processing theory. This research can help inform curriculum decisions with regard to early literacy.
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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 | 0.001 | 0.010 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".