Bibliographic record
Abstract
espanolEl proposito de compartir es construir conjuntos equivalentes, un contexto ideal para el analisis de importantes conceptos cuantitativos como contar, equivalencia y cardinalidad. Mediante dos estudios se analizo como ninos de cuatro y cinco anos de edad comparten bloques en condiciones de reparto equitativo y de reciprocidad (en esta segunda situacion, un muneco partia, antes del reparto, con el doble de elementos que el otro) y se analizaron tambien sus inferencias cuantitativas realizadas sobre una coleccion, despues de contar los bloques de la otra. La investigadora pidio a los ninos que repartieran bloques dobles e individuales entre dos personajes. Los ninos tuvieron mas exito en la situacion de reparto equitativo que en la de reciprocidad. La mayoria de los ninos que tuvieron exito al compartir tambien hicieron inferencias numericas apropiadas. En el segundo estudio se emplearon monedas canadienses de uno y dos dolares para examinar la importancia de las senales perceptivas al compartir. Mientras que los bloques dobles tienen el doble de tamano de los individuales, no hay senales perceptivas que permitieran a los ninos comparar las monedas porque son todas del mismo tamano. Contrariamente a las expectativas, compartir con las monedas no fue mas dificil que con los bloques y la mayoria de los ninos realizaron inferencias numericas EnglishThe purpose of sharing is to construct equivalent sets, making it an ideal context for analysing important quantitative concepts such as counting, equivalence and cardinality. Two studies analysed how four- and five-year-olds shared blocks in equal sharing and reciprocity conditions and their number inferences about one set after counting the other. The researcher asked children to share double and single blocks between two characters. They succeeded more in building equivalent shares in an equal sharing than reciprocity condition. Most children who shared correctly also made appropriate number inferences. To examine whether perceptual cues helped children share the blocks, a second study used Canadian $1 and $2 coins. A double block is twice the size of a single, whereas there is no visual cue about the value relation between coins because they are the same size. Unexpectedly, children shared equally well with blocks and coins, and most children made number inferences
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.002 |
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".