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Record W2151044770 · doi:10.1177/0165551511406063

Modelling ancient Chinese time ontology

2011· article· en· W2151044770 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

VenueJournal of Information Science · 2011
Typearticle
Languageen
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsMcGill University
FundersDefense Advanced Research Projects Agency
KeywordsReignEmperorOntologyPeriod (music)HistoryEvent (particle physics)Scale (ratio)Computer scienceChinese philosophyHistory of ChinaData scienceAncient historyGeographyArchaeologyChinaCartographyEpistemologyPhilosophyLaw

Abstract

fetched live from OpenAlex

Temporal information is one of the essential components in many domains, especially those related to history. Up until the twentieth century, the Chinese used a lunisolar calendar with the title of an Emperor and a reign period to express temporal information. When describing a historical event in Chinese history, it is inadequate to use existing time ontologies as presented in the traditional Chinese way of thinking to capture and encode time. To date, no attention in the field has been given to modelling ancient Chinese time. In this paper, we identify the problems encountered when modelling Chinese time resulting from the distinctive nature of a non-western time scale. We design a new model of temporal information with combined approaches, which are more appropriate for Chinese dynasties, emperors, and reign periods, and apply the OWL-Time ontology onto the ancient Chinese lunisolar calendar. This approach can also be applied to other ancient time-keeping methods in non-western time scales.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.845
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.009
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 head0.032
GPT teacher head0.255
Teacher spread0.223 · 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