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Record W3115915449 · doi:10.3968/11910

The Differences of Family Education Between China and America

2020· article· en· W3115915449 on OpenAlexvenueno aff
Huihui Guo

Bibliographic record

VenueStudies in literature and language · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Methods and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsChinaCreativityChinese familyFamily valuesPsychologyIndependence (probability theory)Face (sociological concept)Family lifeQuality (philosophy)SociologyGender studiesSocial psychologyPolitical scienceSocial scienceLaw

Abstract

fetched live from OpenAlex

Nowadays, children live in a more privileged life and are provided by family and society with more advantageous conditions. At the same time, social development needs children have high qualified talents to face such environment,then good family education is the key point to the high qualified talents. Because family is the first place that children receive education, which plays an important role in the intellectual development and the quality of the children. There are many types of family education in the world and each of them shows distinctive features and closely relate to its culture. And American family education is the most famous one among them. There are great differences in the concepts, methods and results of education between America and China. My paper will analyze those differences from the three aspects, especially the differences on cultivating children’s independence, creativity and relationships between children and parents of the two kinds of family education. I hope to find a satisfied family education method through comparing the differences between Chinese and American family education so as to provide a helpful way for Chinese family education.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.058
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.066
GPT teacher head0.458
Teacher spread0.392 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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