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Record W4416673026 · doi:10.1017/s0260210525101514

Analysing recognition multidirectionally: Canada, United Nations membership, and the Algerian question

2025· article· en· W4416673026 on OpenAlexafffundabout
Caroline Dunton

Bibliographic record

VenueReview of International Studies · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical and Contemporary Political Dynamics
Canadian institutionsQueen's University
FundersQueen's University
KeywordsIndependence (probability theory)State (computer science)Resistance (ecology)Process (computing)

Abstract

fetched live from OpenAlex

Abstract The process of admitting new members to the United Nations has historically been contentious and contradictory. This paper examines new membership through the lens of recognition, focusing on the historical case of Canada’s role in this debate between 1955 and 1962. Canada led the initiative to grant membership to 16 members in 1955 and supported new membership for 17 others (including former French colonies) in 1960. Simultaneously, it opposed resolutions in the UN General Assembly supporting Algerian independence from France . The concept of recognition helps explain this inconsistency, while this case also reveals much about recognition itself. I argue that recognition, both thick and thin, can be multidirectional, in that granting recognition to another state is part of that state’s own struggle for recognition. In 1955 and 1960, Canada granted thin recognition to new members, which had implications for its own struggle for thick recognition. With Algerian independence, this was a debate about thick recognition for Algeria and for France; Canada’s complex struggle for thick recognition also drove its resistance to recognizing Algeria.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.037
GPT teacher head0.292
Teacher spread0.255 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2025
Admission routes3
Has abstractyes

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