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Record W1488568773 · doi:10.1017/upo9788175968707

Learning from the Field

2008· book· en· W1488568773 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFoundation Books · 2008
Typebook
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsInternational Development Research Centre
Fundersnot available
KeywordsBeijingChinaCitizen journalismParticipatory action researchNatural resourceRural developmentPolitical scienceCurriculumAgricultureField (mathematics)PedagogySociologyGeography

Abstract

fetched live from OpenAlex

Across China, university staff, researchers, students, and farmers are joining forces to bring to the fore action and field-based learning as a way to promote rural development studies. Learning from the Field: Innovating China’s Higher Education System presents first-hand experience and lessons from an innovative, participatory curriculum development initiative in China. It includes the content of two novel courses, ‘Community Based Natural Resource Management’ and ‘Participatory Rural Development’. The first versions of these courses were delivered at the College of Humanities and Development of the China Agricultural University in Beijing in the spring of 2005 and at the Jilin Agricultural University in Changchun in the spring of 2006.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.240
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.001

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 head0.028
GPT teacher head0.304
Teacher spread0.276 · 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