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Record W4230377400 · doi:10.31234/osf.io/zqpdw

Cracking the code of place value: The relationship between place and value takes years to master

2020· preprint· en· W4230377400 on OpenAlexaff
Pierina Cheung, Daniel Ansari

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsWestern University
Fundersnot available
KeywordsNumerical digitConstruct (python library)Value (mathematics)Position (finance)NotationArithmeticBase (topology)PsychologyMathematicsStatisticsComputer scienceProgramming language

Abstract

fetched live from OpenAlex

Place value, which underlies the meanings of multi-digits, encompasses the principle of position and base-10 rules. To understand “65,” one needs to know that the digits “6” and “5” occupy different positions and thus represent ordered values of different magnitudes (the principle of position), and that the value of each position is determined by base-10 rules (e.g., the rightmost position is 10^0, followed by 10^1, 10^2, etc.). Without the principle of position, children cannot construct meanings for multi-digits. Previous studies show that children do not know the exact value of digit positions until early elementary school years, but less is known about the acquisition of positional knowledge for multi-digits. To study when and how children construct a relationship between position and value, this study explored when children begin to know that the leftmost digit represents the largest value, and whether such knowledge relates to learning number names. Four to 7-year-olds from primarily Caucasian, middle-class families were asked to compare different pairs of multi-digits. Some comparisons (e.g., 12 vs. 21) required knowledge of positional property, and some did not (e.g., 35 vs. 36). We found that as a group, 6-year-olds could recruit positional knowledge to compare multi-digits. We also found that children who knew the number names of both of the multi-digits in a comparison pair were above chance on multi-digit comparison. Our results shed light on the developmental steps towards acquiring place-value notation, and highlight a role of learning number names for learning positional property of the place-value notation.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0030.008
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.139
GPT teacher head0.343
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2020
Admission routes1
Has abstractyes

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