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Record W2460155158 · doi:10.1080/00438243.2016.1184586

‘I have better stuff at home’: treasure hunting and private collecting of World War II artefacts in Finnish Lapland

2016· article· en· W2460155158 on OpenAlexfundno aff
Vesa‐Pekka Herva, Eerika Koskinen-Koivisto, Oula Seitsonen, Suzie Thomas

Bibliographic record

VenueWorld Archaeology · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeological Research and Protection
Canadian institutionsnot available
FundersKulttuurin ja Yhteiskunnan Tutkimuksen ToimikuntaAcademy of FinlandOulun YliopistoMcGill UniversityHelsingin Yliopisto
KeywordsTreasureAmateurContext (archaeology)GermanArchaeologyPeriod (music)HistoryWorld War IIPoint (geometry)Visual artsGeographyArtAesthetics

Abstract

fetched live from OpenAlex

Almost all archaeologists encounter collectors of different kinds of artefacts at some point in their career, whether it is the private collectors of financially valuable antiquities or ‘amateur archaeologists’ who have amassed personal collections of local finds. In our research into the material legacy of the German presence in northern Finland during World War II, we have encountered both artefact hunters (primarily but not exclusively metal detecting enthusiasts) and artefact collectors (sometimes the same people) with a specific interest in military remains from this location and period. In this article, we explore these alternative perspectives on collecting, and frame them within the context of treasure hunters, militaria collectors and other history hobbyists, and their relationship to the ‘official’ heritage managers and curators.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.052
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0140.009
Scholarly communication0.0060.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.240
Teacher spread0.216 · 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 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

Citations29
Published2016
Admission routes1
Has abstractyes

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