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Record W7113185159

A roma népesség számának prognózisa, a romák által felülreprezentált megyékben 2061-ig = Projection of Roma population in overrepresented counties until 2061

2020· article· hu· W7113185159 on OpenAlexaboutno aff

Bibliographic record

VenueRepository of the Academy's Library (Library of the Hungarian Academy of Sciences) · 2020
Typearticle
Languagehu
FieldHealth Professions
TopicRomani and Gypsy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCensusPopulationProjection (relational algebra)Quarter (Canadian coin)InequalityPopulation projectionDemographic analysis
DOInot available

Abstract

fetched live from OpenAlex

A roma nemzetiségű népesség az 1990-es népszámlálásban 142 683 fő, míg 2001-ben 189 984 fő volt. Ezzel szemben a Kemény István nevéhez fűződő cigány összeírások szerint – amely a lakókörnyezet véleménye alapján készült – a roma lakosság létszáma 1993-ban 467 000, 2003-ban pedig 569 000 fő volt. Utóbbi két adatból a Hablicsek László által 1991-re és 2001-re visszaszámolt létszám 448 100 fő, illetve 549 700 fő volt. Ezek szerint a lakókörnyezet véleménye alapján 1991-ben több mint 3-szor, de 2001-ben is 2,9-szer akkora volt a cigányság létszáma, mint amekkora a népszámlálások szerint. Ha igazolódik az az előfeltevés, hogy a lakókörnyezet véleménye alapján minősített roma népesség jelentős részben hátrányos helyzetű, akkor a népszámlálásokban szereplő roma nemzetiségű népességhez képest egy jóval nagyobb, mintegy háromszor akkora népességcsoporttal kell foglalkozni. | In the 1990 census, the Roma population was 142,683, while in 2001 it was 189,984. However, according to Roma enumerations associated with István Kemény – based on neighborhood perceptions – the Roma population was 467,000 in 1993 and 569,000 in 2003. Based on this, László Hablicsek retroactively estimated 448,100 for 1991 and 549,700 for 2001. These figures imply that the perceived Roma population was over three times higher in 1991 and almost three times higher in 2001 than official census numbers. If the assumption holds that this perceived group is largely disadvantaged, policies should address a population nearly three times larger than census figures suggest.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesScience and technology studies, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.005
Science and technology studies0.0020.003
Scholarly communication0.0000.007
Open science0.0050.004
Research integrity0.0010.003
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 head0.053
GPT teacher head0.317
Teacher spread0.263 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

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