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Record W2261691020 · doi:10.1080/00438243.2015.1100548

Ingroup identification, identity fusion and the formation of Viking war bands

2015· article· en· W2261691020 on OpenAlexafffund
Ben Raffield, Claire Greenlow, Neil Price, Mark Collard

Bibliographic record

VenueWorld Archaeology · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEvolutionary Game Theory and Cooperation
Canadian institutionsSimon Fraser University
FundersBritish Columbia Knowledge Development FundSocial Sciences and Humanities Research Council of Canada
KeywordsIdentity (music)Identification (biology)Ingroups and outgroupsIdentity formationCollective identityLoyaltyHistorySociologyEpistemologyAestheticsPsychologyLawSocial psychologyPhilosophyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

The lið, a retinue of warriors sworn to a leader, has long been considered one of the basic armed groups of the Viking Age. However, in recent years the study of lið has been eclipsed by the discussion of larger Viking armies. In this paper, we focus on the key question of how loyalty to the lið was achieved. We argue that two processes that have been intensively studied by psychologists and anthropologists – ingroup identification and identity fusion – would have been important in the formation and operation of lið. In support of this hypothesis, we outline archaeological, historical and literary evidence pertaining to material and psychological identities. The construction of such identities, we contend, would have facilitated the formation of cohesive fighting groups and contributed to their success while operating in the field.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.010
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.296
Teacher spread0.273 · 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 designTheoretical or conceptual
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

Citations74
Published2015
Admission routes2
Has abstractyes

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