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Record W2066079995 · doi:10.1080/13892240701820207

Introducing Participatory Curriculum Development in China's Higher Education: The Case of Community-Based Natural Resource Management

2008· article· en· W2066079995 on OpenAlexaff
Gubo Qi, Xiuli Xu, Ting Zuo, Xiaoyun Li, Chen Keke, Ji Miao, Lin Liu, Mao Miankui, LI Jing-song, Yiching Song, Long Zhipu, Min Lu, Yuan Juanwen, Ronnie Vernooy

Bibliographic record

VenueThe Journal of Agricultural Education and Extension · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsInternational Development Research Centre
Fundersnot available
KeywordsCurriculumChinaNatural resource managementCitizen journalismSociologyNatural resourceCurriculum developmentPedagogyProcess (computing)Professional developmentParticipatory action researchEngineering ethicsPolitical scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

Abstract This article describes and reflects on a novel course developed at China Agricultural University to introduce Community-Based Natural Resource Management at the postgraduate level. This course, part of a larger educational renewal initiative addressing the current reform of China's higher education system, was developed through a participatory curriculum development methodology bringing together teachers and researchers from five different organizations, as well as a dynamic group of MSc and PhD students. The course development process and the actual delivery in the classroom and in the field were guided by insights from adult teaching and learning theory. These were adapted to the Chinese reality. Results assessed to date from the experimentation with this completely new approach in China encompass conceptual, attitudinal, methodological, and practical changes. The experiences and insights accumulated so far serve as entry points for the expansion of participatory curriculum development practice in China. They also provide a ground for deepening learning theory.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.044
GPT teacher head0.335
Teacher spread0.290 · 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 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

Citations16
Published2008
Admission routes1
Has abstractyes

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