Building for the future by expatiating the past: high drama from the summit of China’s learning mountain
Bibliographic record
Abstract
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?
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".