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Record W2904381510 · doi:10.1177/0257643018810040

From Kingdoms to Transregional States: Exploring the Dynamics of State Formation in Pre-modern Odisha

2018· article· en· W2904381510 on OpenAlexaboutno aff
Bhairabi Prasad Sahu

Bibliographic record

VenueStudies in History · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSouth Asian Studies and Conflicts
Canadian institutionsnot available
Fundersnot available
KeywordsLegitimationState formationState (computer science)PoliticsVernacularAgrarian societyHistoryFellKingdomCasteQuarter (Canadian coin)Ancient historyPolitical economySociologyPolitical scienceGenealogyGeographyArchaeologyLawAgriculturePhilosophyLinguisticsGeology

Abstract

fetched live from OpenAlex

In what is today known as Odisha and in its adjoining areas, the closing centuries of the first millennium ce and beyond were marked by the shift from usual kingdoms to larger and more complex state systems, spread over several subregions/regions ( maṇḍalas). The socio-economic and cultural processes—ranging from agrarian growth and the rise of markets, merchants and towns to the shaping of a region-specific caste system and vernacular language and literature—which sustained these political developments and the new requirements such as the elaboration in the structure of administration and legitimation constitute the subject matter of this article. The transregional states under discussion are somewhat comparable with imperial formations insofar as they were conquest states and perpetuated unevenness and differences between spaces, peoples and cultures across the constituent spatial segments. However, in terms of their territorial dimensions and resources, they fell short of the empires and therefore may be seen to be located between the usual kingdoms and celebrated empires.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.014
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.134
GPT teacher head0.341
Teacher spread0.208 · 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 designQualitative
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
Published2018
Admission routes1
Has abstractyes

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