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Record W4297185068 · doi:10.1177/00219096221124937

Assessment of Socioeconomic Development of the Aspirational District in Central India: A Methodological Comparison

2022· article· en· W4297185068 on OpenAlexaboutno aff
Vikrant P. Katekar, Sandip Deshmukh

Bibliographic record

VenueJournal of Asian and African Studies · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsSocioeconomicsPopulationCasteGeographySocioeconomic statusTribeLiteracy rateLiteracyFirewoodQuarter (Canadian coin)AgricultureStandard of livingAgricultural economicsDemographyEconomic growthEconomicsSociologyPolitical science

Abstract

fetched live from OpenAlex

This study examines the Washim district’s socioeconomic growth to determine the criteria for district development. This study found that the Washim district comes under the rural area, where the child sex ratio is inferior. Even in some villages, it has zero value. In some regions where the Scheduled Caste (SC)/Scheduled Tribe (ST) population is more, the child sex ratio is less. Out of six sub-districts, four sub-districts have a low literacy rate (less than 75%). The mean literacy rate is found as 81.7%, and its standard deviation is evaluated as 7.0. In the region where the literacy rate is higher, people are less busy with farming, and hence the percentage of the cultivator is less and vice versa. In the region where SC and ST population is more, the percentage of Household Industry (HHI) worker is higher and vice versa. Out of 698 inhabited villages, 692 villages are electrified in the district, with 99.1% of towns having installed power supply. The use of liquefied petroleum gas (LPG)/piped natural gas (PNG) is minimal in the Washim district. In the region where firewood availability is accessible and adequate, the use of LPG/PNG is found to be less than its average value.

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)0.001
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 score0.020
Threshold uncertainty score0.174

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.072
GPT teacher head0.330
Teacher spread0.258 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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