Bibliographic record
Abstract
In order to understand how infants come to understand others' intentions we need first to study how intentional engagements occur in early development. Engaging with intentions requires that they are, first of all, potentially available to perception and, second, that they are meaningful to the perceiver. I argue that in typical development it is in the infant's responses to others' infant-directed intentional actions that others' intentions first become meaningful. And that it is through the meaningful joining of intentions that understanding continues to develop. I use three common arenas in the first year to illustrate this claim: infants'anticipatory adjustments to being picked up, infants' emerging compliance to others' directives, and infant teasing. Even by the age of two months infants adjust their postures appropriately, gazing at the adult's face as they approach with arms outstretched to pick them up. From the middle of the first year infants come to recognize the meanings of verbal directives and start to comply with them, being drawn further into the cultural worlds of their families. In the last quarter of the first year infants start to playfully tease and foil others' intentions in a variety of ways, actively redirecting the course of intentional engagements. Others' intentions are thus increasingly available to infants, allowing cooperation, challenge, and further elaboration. Joint intentional actions are best understood as the processes through which intention awareness develops rather than just as the products of such awareness.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".