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Record W2494057047 · doi:10.1017/cbo9780511523953.005

Nature of the borderland

2006· book-chapter· en· W2494057047 on OpenAlexaff
Leo K. Shin

Bibliographic record

VenueCambridge University Press eBooks · 2006
Typebook-chapter
Languageen
FieldArts and Humanities
TopicLandscape and Cultural Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGeographyHistoryGeology

Abstract

fetched live from OpenAlex

The northeastern part [of Guangxi] is civilized. Its air is pure and fine. As a result, its people are refined and cultured. The southwestern part is uncivilized. … Its air is malarial. As a result, its people are ferocious and crude. General Gazetteer of Guangxi (1531) Some time during the tenth year of the reign of the Chenghua emperor (1474 in conventional rendering), a certain Li Zongxian of Changzhou prefecture (Southern Metropolitan Region) was handed the unenviable assignment of serving as prefect of Xunzhou in central Guangxi. Unless Li was exceptionally brilliant or especially unfortunate, the prefect-designate was probably in his late twenties or early thirties in 1460 when he obtained the much-esteemed metropolitan graduate ( jin shi ) degree. It is unclear what Li's earlier appointments had been or how well he had performed in those capacities, but as he was ranked a middling 62 (out of 156) in the metropolitan examinations, it was unlikely he would have been placed on a career fast track in the Ming officialdom. Although the post of prefect was accompanied by a respectable rank of 4a (on a descending scale from 1a to 9b), it is not difficult to imagine how disappointed Li Zongxian must have been, especially when one realizes that the prefecture of Xunzhou had been the site of major clashes between the Ming military and the local “Yao” population less than a decade earlier and that neither his predecessor nor successor for the position even possessed the jin shi degree.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.020
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.001

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 head0.014
GPT teacher head0.165
Teacher spread0.150 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2006
Admission routes1
Has abstractyes

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