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Record W4389987847 · doi:10.18732/hssa95

Calendars, Compliments, and Computations

2023· article· en· W4389987847 on OpenAlexvenueno aff
Anuj Misra, Jean Arzoumanov

Bibliographic record

VenueHistory of Science in South Asia · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHistorical Astronomy and Related Studies
Canadian institutionsnot available
FundersSaint Petersburg State University
KeywordsSanskritEmperorPersianSalientTransliterationLiteratureHistoryLinguisticsPhilosophyAncient historyArtArchaeology

Abstract

fetched live from OpenAlex

Various studies in recent times have shown how sociohistorical proclivities played an important role in the acceptance or rejection of cross-cultural ideas in Mughal scientific discourses. The cultural patronage of the Mughal courts financed the production and propagation of certain scientific texts deemed intellectually and politically expedient. Among such texts were two seventeenth-century astronomical table-texts, Mullā Farīd's Persian Zīj-i Šāh Jahānī and its Sanskrit translation in Nityānanda's Siddhāntasindhu, both produced at the court of the Mughal Emperor Šāh Jahān. In this paper, we present, for the very first time, a comparative survey of the canon (text) of these two works to reveal the intimacy between the translated Sanskrit and its Persian original. The paper includes brief biographies of both astronomers, a summary of the salient features of the canons, a description of the manuscripts utilised and our transcription and transliteration schemes, along with a detailed comparison of the individual chapters in these canons. We also provide separate appendices with discussions on select aspects from these chapters. We note that this paper forms the first part in a two-part study, with a second forthcoming paper surveying the tables in these two texts (accompanied with mathematical annotations).

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.270
Threshold uncertainty score0.464

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 studies0.0000.001
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 head0.020
GPT teacher head0.244
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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
Published2023
Admission routes1
Has abstractyes

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