MétaCan
Menu
Back to cohort
Record W2116024198 · doi:10.7202/038930ar

Nagorno-Karabakh

2010· article· fr· W2116024198 on OpenAlexaffvenue
Oana Tranca

Bibliographic record

VenueÉtudes internationales · 2010
Typearticle
Languagefr
FieldSocial Sciences
TopicPost-Soviet Geopolitical Dynamics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHumanitiesPolitical scienceDe factoArtLaw

Abstract

fetched live from OpenAlex

Les conflits gelés constituent aujourd’hui un défi majeur à la stabilité et à la sécurité internationales. Exemple typique d’un conflit gelé, le cas du Nagorno-Karabakh présente également des spécificités. Cette étude consiste en deux principaux volets. Il s’agit d’abord de circonscrire la spécificité du Nagorno-Karabakh dans le contexte plus large des conflits gelés et des États sécessionnistesde facto. L’article contribue ensuite à l’élaboration d’un cadre d’analyse plus large qui évalue les étapes menant à un conflit gelé, au vu des différentes phases de sa transformation : escalade, diffusion et intervention des tierces parties. Les conclusions de cette étude de cas mettent en relief des leçons pouvant contribuer à la mise en place de stratégies visant la prévention de l’émergence des conflits gelés.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.053
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.005
Scholarly communication0.0030.001
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.001

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 head0.021
GPT teacher head0.324
Teacher spread0.303 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueÉtudes internationalesSame topicPost-Soviet Geopolitical DynamicsFrench-language works237,207