MétaCan
Menu
Back to cohort
Record W3091676103 · doi:10.1163/15685373-12340090

Children’s Ethno-National Flag Categories in Three Divided Societies

2020· article· en· W3091676103 on OpenAlexfundno aff
Jocelyn Dautel, Edona Maloku, Ana Tomovska Misoska, Laura K. Taylor

Bibliographic record

VenueJournal of Cognition and Culture · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsnot available
FundersQueen's UniversityQueen's University BelfastBritish Psychological SocietyDepartment for the Economy
KeywordsFLAGS registerFlag (linear algebra)NationalityCategorizationAllegianceEthnic groupNationalismNational consciousnessPsychologySocial psychologyPolitical sciencePoliticsImmigrationLinguisticsLaw

Abstract

fetched live from OpenAlex

Abstract Flags are conceptual representations that can prime nationalism and allegiance to one’s group. Investigating children’s understanding of conflict-related ethno-national flags in divided societies sheds light on the development of national categories. We explored the development of children’s awareness of, and preferences for, ethno-national flags in Northern Ireland, Kosovo, and the Republic of North Macedonia. Children displayed early categorization of, and ingroup preferences for, ethno-national flags. By middle-childhood, children’s conflict-related social categories shaped systematic predictions about other’s group-based preferences for flags. Children of minority-status groups demonstrated more accurate flag categorization and were more likely to accurately infer others’ flag preferences. While most Balkan children preferred divided versus integrated ethno-national symbols, children in the Albanian majority group in Kosovo demonstrated preferences for the new supra-ethnic national flag. We discuss the implications of children’s ethno-national flag categories on developing conceptualizations of nationality and the potential for shared national symbols to promote peace.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score0.183

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 science0.0000.000
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 head0.044
GPT teacher head0.330
Teacher spread0.286 · 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
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

Citations7
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Cognition and CultureSame topicSocial and Intergroup PsychologyFrench-language works237,207