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Record W2899929804 · doi:10.29173/psur27

He’s Just Not That Into Yu(goslavia)

2016· article· en· W2899929804 on OpenAlexvenueno aff
Jelena Macura

Bibliographic record

VenuePolitical Science Undergraduate Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicBalkans: History, Politics, Society
Canadian institutionsnot available
Fundersnot available
KeywordsSerbianHatredRhetoricNationalismContext (archaeology)Political scienceScholarshipPopulationDreamLawSociologyPolitical economyHistoryLinguisticsPoliticsPsychologyPhilosophyDemography

Abstract

fetched live from OpenAlex

The Former Socialist Federal Republic of Yugoslavia is an interesting case study that is applicable to various aspects of international relations scholarship. During a time where different regions struggled to coexist, questions of nationalism and ethnicity evolved into conflict. Slobodan Milošević was a Serbian politician, and capitalizing on the discontent of the Serbian nation, rallied support, and mobilized an army dedicated to achieving the dream of a “Greater Serbia”. It can be argued that rhetoric and discourse played an important role in formulating the view of a superior Serbian nation, while assembling a population ready for war. Long after Milošević’s death, his words still resonate with the Serbian nation, and severely impede reconciliation efforts. To illustrate how ancient hatred prevents states from moving forward, in the Serbian context, this essay specifically takes into consideration Milošević’s 1987 Kosovo Polje Speech and his 1989 Gazimestan address.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.093
GPT teacher head0.388
Teacher spread0.295 · 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 designTheoretical or conceptual
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

Citations1
Published2016
Admission routes1
Has abstractyes

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