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Record W2016543444 · doi:10.5539/jsd.v8n4p70

Employment of Geoinformation Technologies in Historical Researches Experience of Kazan (Volga Region) Federal University

2015· article· en· W2016543444 on OpenAlexvenueno aff
Dina A. Mustafina, Olga V. Luneva, Luiza Kayumovna Karimova

Bibliographic record

VenueJournal of Sustainable Development · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsnot available
FundersKazan Federal University
KeywordsCensusDigitizationRegional scienceGeographyGeographic information systemCadastreHuman settlementSettlement (finance)PopulationHistorical geographyArchaeologyCartographyEconomic geographySociologyComputer scienceDemography

Abstract

fetched live from OpenAlex

Relevance of the topic is due to the prevailing in modern science trend - complex study of society, not only in time but also in space, established thanks to the convergence and integration of Arts and Sciences. The paper aims to describe our own experience of association within the framework of a single information field of full-text data sources, mainly registration, accounting and statistics data (cadastres, scribe and census books) and maps to represent in terms of spatial coordinates the social stratification of the city Sviyazhsk and its surroundings in the XVII century. The leading method in the research of this problem is that of geo-information technologies. The main results of this work were the establishment of historical sources database of the period under study, data formalization of the written sources, compiling the geographic basis of geo-information system (GIS), digitization and creation of vectorized base of geo-data (BGD), which allows to localize and identify the settlement of Sviyazhsk County. The paper materials will be useful for further research and for model development of a settlement structure, farm tenure and land use, social stratification of the population and multifaceted relationships of towns and villages (peasantry) in the Middle Volga area in the second half of the XVII century.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.326
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.065
GPT teacher head0.293
Teacher spread0.228 · 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 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
Published2015
Admission routes1
Has abstractyes

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