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Record W28484410 · doi:10.3389/fpsyg.2017.00609

Peningkatan Kualitas Kerja Berdasarkan Aspek Ergonomika pada Packing and Vanning Division PT. Toyota Motor Manufacturing Indonesia, Sunter 1 Plant Jakarta.

2009· article· en· W28484410 on OpenAlexfundno aff
Ali Masrochan

Bibliographic record

VenueFrontiers in Psychology · 2009
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Management
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDivision (mathematics)EngineeringMathematicsArithmetic

Abstract

fetched live from OpenAlex

We examined how naming objects with unique labels influenced infants' reasoning about the non-obvious properties of novel objects. Seventy 14- to 16-month-olds participated in an imitation-based inductive inference task during which they were presented with target objects possessing a non-obvious sound property, followed by test objects that varied in shape similarity in comparison to the target. Infants were assigned to one of two groups: a <i>No Label</i> group in which objects were introduced with a general attentional phrase (i.e., "Look at this one") and a <i>Distinct Label</i> group in which target and test objects were labeled with two distinct count nouns (i.e., <i>fep</i> vs. <i>wug</i>). Infants in the Distinct Label group performed significantly fewer target actions on the high-similarity objects than infants in the No Label group but did not differ in performance of actions on the low-similarity object. Within the Distinct Label group, performance on the inductive inference task was related to age, but not to working memory, inhibitory control, or vocabulary. Within the No Label condition, performance on the inductive inference task was related to a measure of inhibitory control. Our findings suggest that between 14- and 16-months, infants begin to use labels to carve out distinct categories, even when objects are highly perceptually similar.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.399
Teacher spread0.361 · 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 teacher head, not a consensus.

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
Published2009
Admission routes1
Has abstractyes

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