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Record W2618673561 · doi:10.4995/var.2017.6056

Getting to the point: making, wayfaring, loss and memory as meaning-making in virtual archaeology

2017· article· en· W2618673561 on OpenAlexaff
Michael Carter

Bibliographic record

VenueVirtual Archaeology Review · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMateriality (auditing)Meaning (existential)Agency (philosophy)Meaning-makingComputer sciencePoint (geometry)Object (grammar)ArchaeologyAestheticsSociologyHistoryEpistemologyLinguisticsArtArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

The initial construction of a digital virtual object is the three-dimensional (3D)point. Using the notions of making, wayfaring, meshwork and agency, this discussion focuses on Ingold’s (2011) theoretical approach to these comments as a means for the construction of archaeological knowledge as applied to the 3D virtual landscape. It will demonstrate that 3D points, whether constructed or captured, can be considered to be agents within an actor network, have agency and are subject to memory and loss within the digital archaeological record. By their interconnections they become a mesh work that can exchange and retain unique attributes of materiality. As such, they challenge our notions of meaning-making beyond the rote actions of visualizing within archaeology to a form that is more theoretically deeper. By viewing the construction and capture and the production of 3D or 2D visual data through a different lens but within theoretical archaeological terms, we can begin to understand our role in the creation of meaning within virtual archaeology.

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)0.003
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 score0.600
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.029
GPT teacher head0.288
Teacher spread0.259 · 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

Citations10
Published2017
Admission routes1
Has abstractyes

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