MétaCan
Menu
Back to cohort
Record W2973173833 · doi:10.1017/9781316827437.007

China, c. 600–c. 1700

2019· book-chapter· en· W2973173833 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

VenueCambridge University Press eBooks · 2019
Typebook-chapter
Languageen
FieldArts and Humanities
TopicHistorical Linguistics and Language Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEmpireScholarshipChinaPeriod (music)UnificationHistoryLexicographyAncient historyHistory of ChinaLiteratureArtLinguisticsPolitical scienceLawPhilosophyArchaeologyComputer scienceAesthetics

Abstract

fetched live from OpenAlex

The story of Chinese lexicography, and indeed of China itself, from the years 600 to 1700 begins and ends in a period of unification. The short-lived Sui dynasty (605–18) unified the Chinese empire after centuries of division. In the wake of a new imperially sponsored examination system came dictionaries aimed at creating a unified standard for exam usage. By the early eighteenth century, the Manchu Qīng dynasty had eliminated most of the vestiges of rebellion from the previous dynasty and was set on expanding its territory. Part of its imperial project was linguistic, as the new multilingual empire staked its claim as an authority on Chinese, among other languages. During this period of more than a thousand years, states and dynasties came and went, and the territory claimed as China fluctuated drastically. A cultural identity, however, came to be maintained, in large part on the basis of a textual tradition. The centrality of texts and language for participating in this culture is reflected in turn by the important place lexicography occupied in the world of Chinese scholarship.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.838
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.023
GPT teacher head0.183
Teacher spread0.160 · 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