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Record W2468555894 · doi:10.1057/9781137425706_6

State Repression in the Civil War’s Aftermath

2015· book-chapter· en· W2468555894 on OpenAlexaff
Gavin M. Foster

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2015
Typebook-chapter
Languageen
FieldSocial Sciences
TopicIrish and British Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsSpanish Civil WarPersecutionImprisonmentState (computer science)ScrutinyPolitical scienceTorturePovertyGovernment (linguistics)HistoryEconomic historyPolitical economyLawCriminologySociologyPoliticsHuman rights

Abstract

fetched live from OpenAlex

The half decade between the ambiguous end of the civil war and the rise of de Valera’s Fianna Fáil party in the late 1920s was a deeply traumatic period for the losers of the conflict. In his oration at the 1924 Wolfe Tone commemoration at Bodenstown, republican propagandist Brian O’Higgins spoke of ‘the cesspools of calumniation … the thorny ways of poverty … the torture-hells called prisons and the bitterness of exile’ that ‘republican idealists’ in every generation had been forced to endure. 1 O’Higgins’ prophetic comments neatly telegraph the central features of republicans’ collective experience living under a newly consolidated post-revolutionary status quo. Stripped of O’Higgins’ literary language, the primary post-revolutionary difficulties republican sources have stressed include ongoing persecution by the state; financial hardship brought about by imprisonment and economic discrimination amidst the depressed postwar economy; and a mass exodus abroad. To what extent does this picture stand up to scrutiny? Were the forces of repression as severe as republicans alleged? Did the losers of the civil war suffer inordinate hardship as a result of an orchestrated campaign of economic victimization? Did republican activists emigrate from the early Free State in especially high numbers? And if so, were government repression and economic victimization the main ‘push factors’ behind this exodus? These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.003
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 score0.011
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.010
Scholarly communication0.0070.002
Open science0.0000.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0080.001

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 head0.037
GPT teacher head0.290
Teacher spread0.253 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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