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Record W2230922464

The Determinants of Foreign Policy Volatility

2014· article· en· W2230922464 on OpenAlexaboutno aff
Eleonora Mattiacci

Bibliographic record

VenueOhioLink ETD Center (Ohio Library and Information Network) · 2014
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy Security and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsVolatility (finance)EconomicsBusinessEconometrics
DOInot available

Abstract

fetched live from OpenAlex

To the casual observer of states' relations, countries like the United States appear to always clearly define other countries as either allies (e.g., Canada) or enemies (Cuba).This neat separation makes it easier to deal with everyday matters such as trade tariffs, because it allows countries to quickly discern whom to sanction and whom to support.Upon closer inspection, however, it appears that relations between states are far more volatile-that is, they are characterized by inconsistent shifts between episodes of intense cooperation and episodes of bitter violence.For instance, in January 2011 Pakistan issued a military threat to India to desist from its nuclear program.A mere week later, India forcibly accused Pakistan of harboring terrorist attacks on Indian soil.In April of the same year, the two countries instituted a joint working group to enhance trade ties between them.Yet, at the beginning of May, India started conducting military exercises at the border with Pakistan, causing Pakistan's violent retaliation.What propels states to embrace volatile foreign policies?This dissertation investigates the presence of volatility in states' foreign policy, and it offers a theory of its determinants.Specifically, it presents a conceptualization of volatile foreign policy as being characterized by inconsistent shifts between To paraphrase Yogi Berra, writing a dissertation is ninety percent mental and the other half is physical.And therefore, throughout the years I've incurred many, many debts.First of all, with my committee members, who have invested a lot of time and energy in training me for this project.The PhD is a medieval institution for many, many aspects, from the most superficial ones (the concept of doctus, from which the word doctorate derives), to the most consequential ones (i.e., the idea of specializing in a topic and researching it extensively).My favorite medieval component of the PhD is the practice of having students apprehend the craft of research from their mentors, as it was in guilds.In my committee, I found invaluable mentors who have pushed me to be a better student, a better scholar, and ultimately a better person.

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)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.211
Teacher spread0.203 · 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 designObservational
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
Published2014
Admission routes1
Has abstractyes

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