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Record W2052388097 · doi:10.1080/02601370600772319

Building for the future by expatiating the past: high drama from the summit of China’s learning mountain

2006· article· en· W2052388097 on OpenAlexaff
Roger Boshier, Yan Huang

Bibliographic record

VenueInternational Journal of Lifelong Education · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDictatorshipSummitPoliticsChinaCommunismDramaHumanismHistorySociologyLawPolitical scienceCartographyGeographyVisual artsDemocracyArt

Abstract

fetched live from OpenAlex

As part of a large‐scale learning initiative, the Chinese Communist Party has declared Lushan to be a ‘learning mountain’. There have been people learning at Lushan Mountain for 2000 years. In 1959 there was a Central Committee meeting at Lushan, where Mao Zedong purged his widely respected comrade Peng Dehuai for daring to say people were starving because of the Great Leap Forward. Everyone knew Peng spoke the truth but few dared antagonize the Chairman. Today the 1959 purge of Peng is seen as the end of comrades and consensus and beginning of dictatorship. There were other tumultuous meetings there in 1961 and 1970. Hence, reconstructing Lushan as a learning mountain is an attempt to expatiate the past and build a more humane future. In addition, putting learning at the top of the mountain brings tourists! The authors analyze the Chinese learning initiative and describe the political significance of Lushan. Theoretically, the learning mountain is shaped by Jiang Zemin’s ‘three represents’, first‐generation (Faure report) lifelong education and, most surprisingly, humanist/interpretivism. At Lushan, the 21st century might best be assured by learning from the first century. Zhu Xi was wise and, 2000 years ago, not enthused by learning in schools. Now as then, why go to school when you can learn on a mountain?

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.008
GPT teacher head0.325
Teacher spread0.317 · 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 designNot applicable
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

Citations7
Published2006
Admission routes1
Has abstractyes

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