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Record W1986155890 · doi:10.1353/mou.2010.0080

Elephants, Alexander and the Indian Campaign

2010· article· en· W1986155890 on OpenAlexvenueno aff
Michael B. Charles

Bibliographic record

VenueMouseion Journal of the Classical Association of Canada · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicAncient Near East History
Canadian institutionsnot available
Fundersnot available
KeywordsMacedonianBattlePossession (linguistics)Context (archaeology)Ancient historyHistoryPhilosophyArchaeology

Abstract

fetched live from OpenAlex

References to elephants captured by Alexander up until the battle of the Hydaspes (326 bc ) are awkward to explain, especially given that the beasts, according to the surviving accounts, filled the Macedonian and allied troops with terror in the same engagement. This is problematic if Alexander’s men had already encountered elephants, even if not in the context of a pitched battle. Moreover, analysis of the evidence suggests that Alexander, far from despising elephants, as our sources, and Curtius Rufus in particular, contend, regarded their possession as both politically and militarily important, and decided to incorporate the animals into the Macedonian army on this basis. Les références aux éléphants capturés par Alexandre avant la bataille de l’Hydaspe (326 avant J. C.) sont difficiles à expliquer, d’autant plus que, selon les récits disponibles, les bêtes remplirent de terreur Macédoniens et troupes alliées au cours de ce même affrontement. Cela pose problème si les hommes d’Alexandre avaient déjà rencontré des éléphants, fût-ce dans un contexte autre que celui d’une bataille rangée. De plus, l’analyse des témoignages suggère que, loin de mépriser les éléphants, comme le prétendent nos sources, et en particulier Quinte-Curce, Alexandre considérait leur possession comme importante d’un point de vue à la fois politique et militaire, et décida de ce fait d’incorporer ces animaux dans l’armée macédonienne.

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)0.001
metaresearch head score (Gemma)0.000
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.517
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.006
GPT teacher head0.169
Teacher spread0.163 · 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
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

Citations5
Published2010
Admission routes1
Has abstractyes

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