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Record W2598177167

Disentangling the effect of students’ maturity on academic achievement

2016· preprint· en· W2598177167 on OpenAlexaboutno aff
Óscar David Marcenaro Gutiérrez, Luis Alejandro López-Agudo

Bibliographic record

VenueRePEc: Research Papers in Economics · 2016
Typepreprint
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Maturity (psychological)Repetition (rhetorical device)Proxy (statistics)AttendancePsychologyReading (process)Argument (complex analysis)Developmental psychologyMathematics educationMathematicsStatisticsGeographyMedicinePolitical scienceLinguistics
DOInot available

Abstract

fetched live from OpenAlex

There exists an increasing number of contributions focused on the influence of the attendance to early childhood and/or preprimary education on the future academic path of the students, which employ in a complementary way the quarter of birth of the student as a proxy for the maturity of the children. The present work goes a step further by making a distinction between three different dimensions of maturity: students’ mental age, proxied by the time when children began to exhibit the basic competences (reading and writing); chronological age, represented by the bimester of birth; and grade repetition, which represents a maturity related to academic knowledge. The suitability of the quarter of birth and the ages of beginning to read and write as an instrument of repetition has been checked, finding that they are not adequate for this purpose This finding might be reinforcing the argument that the ages of beginning to read and write, the quarter of birth and grade repetition might be measuring different dimensions of students’ maturity.

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)0.003
metaresearch head score (Gemma)0.016
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.031
GPT teacher head0.394
Teacher spread0.363 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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