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Record W4291116987 · doi:10.5840/peacejustice20223114

Most Likely to Secede: Can the US “Go Gorbachev”?

2022· article· en· W4291116987 on OpenAlexaboutno aff
Tom H. Hastings

Bibliographic record

VenueJournal for Peace and Justice Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicPost-Soviet Geopolitical Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologySovereigntyIndigenousPolitical scienceAnnexationState (computer science)Political economyIndependence (probability theory)Left-wing politicsLawEconomic historyPoliticsSociologyHistory

Abstract

fetched live from OpenAlex

While secessionists have had their dreams, both furtive and as announced intentions, throughout American history, we may be entering a period of increased fervor for various ideologically driven campaigns that either seek new state boundary shifts, annexation of portions of the US to Canada or other nation-states, or outright sovereignty as new nation-states. The contestation between perfervid far-right ideologues often associated with Donald Trump and a leftist eco-racial justice amalgam of groups and individuals mutually coalescing around complete separation is not unimaginable presently. There are also dreams and talk of complete independence for descendants of slaves and some indigenous tribes. What are the trends and tendencies in the world around these questions and how might they be expressing themselves in the US? If the culture wars militate separations, can we avoid a devolution into US Civil War 2.0?

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.246
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.000
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 head0.052
GPT teacher head0.386
Teacher spread0.335 · 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.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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