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Record W1524159371

On Training Excellent Students in China and the United States

2012· article· en· W1524159371 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueLincoln (University of Nebraska) · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicResearch, Science, and Academia
Canadian institutionsnot available
Fundersnot available
KeywordsChinaCurriculumPolitical scienceStudy abroadSample (material)Medical educationPublic relationsPsychologyPedagogyMedicine
DOInot available

Abstract

fetched live from OpenAlex

In many countries, the training of researchers who will be internationally competitive has become a primary objective, leading to extensive discussion of the curricula, educational content, and methods that may ensure a high level of student achievement. In this global climate, only the most excellent students have the potential to engage successfully in international competition and become leading-edge researchers in the world-wide marketplace of research. Thus, any country seeking to be internationally competitive must consider ways to further raise the level of excellent students. In this study, we investigate university programs, specifically honors programs, that take special measures for training the most excellent students. Honors programs can be found in the United States, Canada, Holland, China, Singapore, Chile, and other countries; among these, the highest number of honors programs are in the U. S. (Digby, 2005) and China. Consequently, the authors chose these two countries as the objects of this study, surveying and comparing the characteristics of honors programs as training courses for excellent students. In both countries, the focus of our study was limited to higher-level universities. In the case of China, only universities identified by Kitagaki & Fuang (2008) as “Key Chinese Universities” were investigated. A small sample of universities in the U. S. was selected from America’s Best Value Colleges (Owens & Meltzer et al., 2006). Our other major sources of information were university websites and the literature available through the National Collegiate Honors Council. In both China and the U. S., honors programs have a common aim to gather and train particularly excellent students in the universities while the specific content of each program and training course is distinct. The characteristics observed in the two countries as well as the comparison of such characteristics may help serve as models for Japan and other countries wishing to create honors programs.

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.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.276
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.101
GPT teacher head0.371
Teacher spread0.269 · 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