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Record W2140855086 · doi:10.5206/cie-eci.v41i2.9203

Teaching in Northwestern China Under a Market Economy: Opportunities and Challenges

2013· article· en· W2140855086 on OpenAlexvenueno aff
Gulbahar H. Beckett

Bibliographic record

VenueComparative and International Education · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Ethnic Minorities and Relations
Canadian institutionsnot available
Fundersnot available
KeywordsOvertimeEarningsChinaJob marketWork (physics)National economyPolitical scienceProfessional developmentPedagogyPsychologyEconomic growthBusinessEconomicsAccountingEconomic systemEngineering

Abstract

fetched live from OpenAlex

This article discusses a case study that explored the impacts of a market economy on some Northwestern Chinese teachers’ working and living conditions as well as opportunities and challenges the new economy presented from teachers’ perspectives. Analysis of surveys, interviews, and documents revealed that the participants believed they had benefited from the market economy, citing pay raises as well as improved working and living conditions. Participants thought opportunities under the market economy included additional earnings as well as improved national and international professional development. However, the participants found the shift from the traditional teacher-centered pedagogy to a more student-centered approach and working with more resourceful students and their parents required constant professional development and overtime work which was challenging and stressful. Findings indicate that the Hanyu (national language also referred to as Putonghua) medium of instruction for minority students, another pedagogical change under the market economy, presented additional challenges to minority teachers who were concerned that the Hanyu medium of instruction may have been an impediment to minority students’ educational achievement and presented serious issues that deserved urgent attention.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.659
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.188
GPT teacher head0.387
Teacher spread0.199 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations3
Published2013
Admission routes1
Has abstractyes

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