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Record W1811646980

Negotiating Nations: Exclusions, Networks, Inclusions — An Introduction

2000· article· en· W1811646980 on OpenAlexvenueaboutno aff
Dirk Hoerder

Bibliographic record

VenueHistoire sociale · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsMulticulturalismNegotiationInclusion (mineral)PoliticsGender studiesInclusion–exclusion principlePower (physics)Political scienceSociologyState (computer science)Political economyLaw
DOInot available

Abstract

fetched live from OpenAlex

The concept of nation is usually understood to include all people within the respective boundaries, and the concept of state to treat all equally. From an analytical perspective, however, these concepts are not mutually reinforcing or even complementary, but contradictory. Political practice and power relationships exclude particular groups because of ethno-culture, religion, gender, class, or “race”. Who belongs, struggles for belonging, or is excluded is a matter of negotiation in power relationships. Non-territorial peoples, diasporic peoples, settled groups who became minorities in larger political entities, working-class men and women, and those regarded as socially inferior have gained admission to national belonging and equal rights only late, or are still struggling for inclusion. An international symposium, “Recasting European and Canadian History: National Con-sciousness, Migration, Multicultural Lives”, brought together scholars from twelve European states and two North American ones to reconsider approaches to migration and the interaction of many cultures in the European past and present. A selection of papers dealing with inclusion in and exclusion from nation-states is presented here.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.708
Threshold uncertainty score0.581

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0110.028
Scholarly communication0.0120.011
Open science0.0020.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.009
GPT teacher head0.236
Teacher spread0.227 · 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

Citations2
Published2000
Admission routes2
Has abstractyes

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Same venueHistoire socialeSame topicCanadian Identity and HistoryFrench-language works237,207