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Record W1919960074 · doi:10.24908/pceea.v0i0.3772

ANCESTRAL ENGINEERING: TOWARDS THE INTEGRATION OF ARCHAEOLOGY AND ENGINEERING

2011· article· en· W1919960074 on OpenAlexaffvenue
David W. Fritz, David S. Strong, Jeff Bryant

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicMetallurgy and Cultural Artifacts
Canadian institutionsNatural Sciences and Engineering Research Council of CanadaQueen's University
Fundersnot available
KeywordsContext (archaeology)SuiteField (mathematics)Engineering ethicsEngineeringArchaeologyHistory

Abstract

fetched live from OpenAlex

We have long felt those who modern society has named “Engineers” have played a significant role in the evolution of cultures and civilizations. Working with manual tools and the materials that nature provided, historical evidence has proven that practical, innovative, and esthetically beautiful creations emerged from our engineering ancestors. As with most effective research, understanding the past can lead to optimizing the future, and we propose that it is beneficial to study engineering and design in this context. In this paper we will discuss what we have termed “Ancestral Engineering”, and describe the rationale behind the initiative. Two main themes have emerged; engineers helping archaeologists to integrate engineering expertise into their investigations, and archaeologists helping engineers to extract engineering design practice and methodology from other cultures. Within this framework, several initial project ideas are discussed and a suite of Research Questions is proposed. We believe this is an emerging field, with significant opportunity to develop collaborative relationships with interested engineers, archaeologists and anthropologists to pursue discussions and potential research in this 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 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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.033
GPT teacher head0.236
Teacher spread0.203 · 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 designObservational
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

Citations3
Published2011
Admission routes2
Has abstractyes

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