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Record W2479366274 · doi:10.1002/9781119055280.ch28

Theoretical Archaeology in India

2016· other· en· W2479366274 on OpenAlexaboutno aff
K. Paddayya

Bibliographic record

Venuenot available
Typeother
Languageen
FieldDecision Sciences
TopicDiverse scientific research topics
Canadian institutionsnot available
Fundersnot available
KeywordsPeasantPrehistoryArchaeologyContext (archaeology)HistoryChalcolithicCitationQuarter (Canadian coin)AnthropologyGeographySociologyLibrary scienceBronze Age

Abstract

fetched live from OpenAlex

Indian prehistory has a long story of 150 years. Up to the mid-1970s, however, the concern of archaeologists was to find secondary-context lithic assemblages in river gravels and silts, from which to build up stratigraphic and cultural sequences based on typological schemes. In protohistory a fine example is provided by the work on the Chalcolithic phase of northern Deccan. In the second quarter of the twentieth century Robert Redfield undertook very detailed studies of the villages of Tepoztlan and Chan Kom to understand the Mayan peasant way of life in Mexico and Hispanic peasant life in Guatemala, respectively. Robert Redfield's writings are both an inspiration and an invitation to archaeologists to adopt a deeper anthropological engagement with the archaeological record of preliterate groups of South Asia, covering both food-producing and hunting-gathering stages. Archaeologists clearly stand to benefit from a closer and fuller understanding of these writings.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0060.008
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.001

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 head0.082
GPT teacher head0.440
Teacher spread0.358 · 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 designTheoretical or conceptual
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".

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Citations0
Published2016
Admission routes1
Has abstractno

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