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Record W7156221474 · doi:10.4324/9781315086538-71

Madhya Pradesh

2017· book-chapter· en· W7156221474 on OpenAlexaboutno aff
Ashok D. Ranade

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldArts and Humanities
TopicSouth Asian Cinema and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsUttar pradeshThe artsState (computer science)AgricultureQuarter (Canadian coin)

Abstract

fetched live from OpenAlex

This largest state in the Indian republic borders on seven other states: Uttar Pradesh, Bihar, Orissa, Andhra Pradesh, Maharashtra, Gujarat, and Rajasthan. Since the country was reorganized along linguistic lines in 1956, Madhya Pradesh has included seventeen Hindi-speaking areas as well as the former Rajasthani-speaking Sironj district. The state comprises twenty former princely states and 1,300 smaller estates ( jāgīr ). These had been seats of patronage for many diverse performing arts traditions, all of which over time contributed to the region’s rich musical and theatrical culture. The seven river systems (Chambal, Betwa, Som, Narmada, Tapi, Mahanadi, and Indravati), together with the imposing Satpura and Vindhya mountain ranges and the tropical forests covering one third of the state, have also had a perennial influence on artistic expression.

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 score0.096
Threshold uncertainty score0.322

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0960.033

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 head0.047
GPT teacher head0.218
Teacher spread0.171 · 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
Published2017
Admission routes1
Has abstractyes

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