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Record W6950505058 · doi:10.5683/sp3/eqoykw

Institutional Trust in the World 1995-2022: Data files and info - world

2024· dataset· en· W6950505058 on OpenAlexaff

Bibliographic record

VenueBorealis · 2024
Typedataset
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsData fileRespondentIdentifierFile formatFlat file databaseFile sharing

Abstract

fetched live from OpenAlex

This dataset includes data files on the individual data of all respondents to international surveys conducted and published between 1995 and 2022, including data on trust in institutions. It also includes answers to three questions about democracy, covering satisfaction, appreciation of the level of democracy, and support for democracy. There are several files: 1) a general file including all combined and harmonized data. 2) a file including the same data but only for respondents who answered at least one question on trust. The next four files allow for a four-level multilevel analysis with HLM, with one file per level: 3) a file at the measurement level (level 1), including one line per trust-related response, per respondent 4) a file including only information on respondents (level 2); 5) a file including information on surveys - and thus on country-year-data source (level 3); 6) a file including information on countries combined with data sources -- region - characteristics of sources. All files include identifiers for the country, year, and data source. Cet ensemble de données comprend les fichiers de données relatifs aux données individuelles de tous les répondants aux sondages internationaux réalisés et publiés entre 1995 et 2022 comprenant des données sur la confiance dans les institutions. Il comprend également les réponses à trois questions sur la démocratie portant sur la satisfaction, l'appréciation du niveau de démocratie et l'appui à la démocratie. Il y a plusieurs fichiers: 1) un fichier général comprenant toutes les données combinées et harmonisées. 2) un fichier comprenant ces mêmes données mais uniquement pour les répondants qui ont répondu à au moins une questions sur la confiance. Les quatre fichiers suivants permettent de faire une analyse multiniveaux à quatre niveaux avec HLM, soit un fichier par niveau 3) un fichier au niveau des mesures (niveau 1), comprenant une ligne par réponse relative à la confiance, par répondant 4) un fichier comprenant uniquement les informations sur les répondants (niveau 2); 5) un fichier comprenant les informations sur les sondages - et donc sur les pays-années-sources de données (niveau 3) 6) un fichier comprenant les informations sur les pays combinés aux sources de données -- région - caractéristiques des sources. Tous les fichiers comprennent des identifiants pour le pays, l'année et la source des données.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.128
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.267
Teacher spread0.248 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2024
Admission routes1
Has abstractyes

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