Bibliographic record
Abstract
先秦诸子常思依附于权力，以至于为得君行道，主动扼杀精神之独立及思想之自由，为权力一统思想，多所谋划。董仲舒“罢黜百家，独尊儒术”，司马谈“同归而殊涂”，向歆父子“诸子出于王官”，使得诸予被重新纳入一统天下的官学架构之中，子学中超出相应官守职掌的天下之学遭到裁抑。与“同归殊涂”的沉沦之路相反，马王堆帛书《要》篇清晰地勾勒了另一“同涂殊归”的超越之路。巫、史、儒都以《周易》占筮，然而其方法与追求迥异。不拘执于具体事件而超越于巫，进入《周易》，通过卦爻的类型化理解，驾驭万事万物，是为史官一道家。不陷溺于繁琐的分类，而追求德义，便能超越于史，而成为儒。毋庸置疑，《要》篇虽然终归亡佚，其思想却死而不亡。
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".