MétaCan
Menu
Back to cohort
Record W2566676815 · doi:10.1080/02601370.2017.1268810

Chinese students speak about their favourite teachers and university reform

2016· article· en· W2566676815 on OpenAlexaff
Roger Boshier

Bibliographic record

VenueInternational Journal of Lifelong Education · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFavouriteBeijingChinaHumanismSociologyGovernment (linguistics)DemocratizationPoliticsGlobalizationPolitical scienceMedia studiesSocial sciencePedagogyDemocracyLaw

Abstract

fetched live from OpenAlex

China is trying to develop better outcomes for students, build world-class institutions and ascend global university rankings. Beijing also wants to jettison the notion it is the workshop of the world and embrace a culture of innovation. Peter Jarvis thinks the world would be a better place if ordinary people were given convivial spaces wherein they can learn and play together. But discourse constructing Chinese universities hardly ever concerns day-to-day life as experienced by students and conviviality is not a priority. This study was a modest attempt to rectify this problem. Students at Chinese universities were asked about their favourite teachers and ideas for university reform. Data were collected in Yangtze Delta universities and in the Beijing region. Students mostly recall their favourite teacher as warm and kind-hearted. Regarding university reform, they want fundamental structural adjustment, democratisation, internationalisation and better integration with society. Chinese government efforts to develop universities are almost entirely located in functionalist notions of globalisation (with ‘socialist characteristics’). In contrast, student preoccupations are nested in humanist and radical humanist paradigms. Tsinghua and Peking universities are now in the ‘top-100’ on Shanghai Jiatong global rankings. However, there is a political chill in China and not much chance of student preoccupations being heard.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.009
GPT teacher head0.338
Teacher spread0.329 · 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 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

Citations4
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Lifelong EducationSame topicHigher Education Governance and DevelopmentFrench-language works237,207