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Actor-Network Theory and the Practice of Aviation Archaeology

2015· article· en· W1552809176 on OpenAlexaffabout
Michael Deal, Lisa M. Daly, Cathy Mathias

Bibliographic record

VenueJournal of Conflict Archaeology · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicAnthropology: Ethics, History, Culture
Canadian institutionsMemorial University of Newfoundland
FundersU.S. Air ForceU.S. Army
KeywordsAviationConflict archaeologyInterpretation (philosophy)ArchaeologyHistoryPerspective (graphical)BattlefieldHistorical archaeologyMaterial culturePrehistoric archaeologyMaritime archaeologySociologyEngineeringVisual artsPrehistoryArtComputer scienceAncient history

Abstract

fetched live from OpenAlex

World War II aviation archaeology is a dynamic subfield of conflict archaeology, which has developed through the need to conserve twentieth-century military heritage resources. Like battlefield archaeology (: iii–vii), it has only recently emerged as a credible area of academic study. Theoretical development in both areas has been dominated by a military, historical-particularist viewpoint, which often ignores the role of society in warfare. Actor-network theory, which stresses the link between society and technology, provides an opportunity to broaden the theoretical perspective of aviation archaeology research. Recent archaeological work at a downed USAAF aircraft site near Gander, Newfoundland, is presented as a case study to illustrate the enlistment of a network of stakeholders, material culture, and textual and audio-visual evidence in the interpretation of a single site.

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)0.020
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.007
Science and technology studies0.0070.055
Scholarly communication0.0110.017
Open science0.0030.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.039
GPT teacher head0.369
Teacher spread0.330 · 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.

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

Citations7
Published2015
Admission routes2
Has abstractyes

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