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Record W2512492340 · doi:10.1037/pac0000185

Measuring the macrosystem in postaccord Northern Ireland: A social–ecological approach.

2016· article· en· W2512492340 on OpenAlexfundno aff
Dana Townsend, Laura K. Taylor, Andrea Furey, Christine E. Merrilees, Marcie C. Goeke‐Morey, Peter Shirlow, E. Mark Cummings

Bibliographic record

VenuePeace and Conflict Journal of Peace Psychology · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentQueen's UniversityQueen's University Belfast
KeywordsEcologyGeographyPolitical scienceBiology

Abstract

fetched live from OpenAlex

The macrosystem refers to the overarching patterns that influence behavior at each level of the social ecology (Bronfenbrenner, 1977), making it a necessary component for assessing human development in contexts of political violence. This article proposes a method for systematically measuring the macrosystem in Northern Ireland that allows for a subnational analysis, multiple time units, and indicators of both low-level violence and positive relations. Articles were randomly chosen for each weekday in 2006-2011 from two prominent Northern Irish newspapers and coded according to their reflection of positive relations and political tensions between Catholics and Protestants. The newspaper data were then compared to existing macro-level measurements in Northern Ireland. We found that the newspaper data provided a more nuanced understanding of fluctuations in intergroup relations than the corresponding measures. This has practical implications for peacebuilding and advances our methods for assessing the impact of macro-level processes on individual development.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.004
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.076
GPT teacher head0.364
Teacher spread0.287 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2016
Admission routes1
Has abstractyes

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