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Record W2124870858 · doi:10.5539/ass.v5n12p87

Education and Transformation of Underachievers in Colleges and Universities

2009· article· en· W2124870858 on OpenAlexvenueno aff
Qida Xu

Bibliographic record

VenueAsian Social Science · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Work Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsVariety (cybernetics)Quality (philosophy)Task (project management)Higher educationPsychologyPower (physics)PedagogyMathematics educationTransformation (genetics)Political scienceSociologyPublic relationsMedical educationManagementComputer scienceLawMedicineEconomics

Abstract

fetched live from OpenAlex

Education and transformation of underachievers is one of focuses and difficulties in education and management of students in higher colleges and universities. Underachievers can be classified into four types of laggard ethic thought, laggard psychological quality, laggard behavioral habits and laggard academic performance. Formation of underachievers is not only due to themselves, but also is closely related to relations with the family, school and the society. Education and transformation of underachievers is a quite complicated and painstaking task, and it is required to mobilize a variety of power from the school, family and the society and to take some comprehensive measures of helping and educating with focus, institutional guarantee, driving by precursors and psychological assistance, etc, so that optimal effects can be achieved.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0030.002
Open science0.0010.006
Research integrity0.0010.001
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 head0.008
GPT teacher head0.305
Teacher spread0.297 · 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 designQualitative
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
Published2009
Admission routes1
Has abstractyes

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