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Record W3039089844 · doi:10.20355/jcie29421

Democratic and Educational Background of Information Poverty: The Case of Turkey

2020· article· en· W3039089844 on OpenAlexvenueno aff
Güler Demir

Bibliographic record

VenueJournal of Contemporary Issues in Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsnot available
Fundersnot available
KeywordsPovertyScope (computer science)DemocracyPoliticsPolitical scienceBasic needsCulture of povertyDevelopment economicsQuality (philosophy)Economic growthSociologyEconomicsLaw

Abstract

fetched live from OpenAlex

Information poverty is one of the most significant characteristics of developing countries and its causes include multiple complex factors, including educational, scientific and technological contexts, political, social and cultural structures, democratic quality, and humanitarian conditions. The purpose of this study is to examine the situation of information poverty in Turkey, focusing on its democratic and educational aspects. In the scope of the study the basic concept of information poverty is briefly introduced. Then, the particular case of Turkey is presented based on domestic and international literature and other public and official sources. The study concludes Turkey is one of the countries which suffers from information poverty, because of failing to fulfill minimal democratic and educational conditions. Associated problems covered do not seem to be solved in the short term. Recommendations are that the first step in the amelioration of information poverty must be awareness-raising by targeting all governmental and societal segments. A multidimensional approach that addresses all segments and policies of the country may be useful, because there is no single factor that explains the information poverty. Finally, librarians and other intellectual workers have a significant role to play in this process.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.470

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 communication0.0000.001
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 head0.022
GPT teacher head0.319
Teacher spread0.297 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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