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Record W3033687227

IDENTIFYING LIABILITY OF FOREIGNNESS USING EBAY AUCTION RESULTS

2005· article· en· W3033687227 on OpenAlexaboutno aff
Stephen S. Standifird

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2005
Typearticle
Languageen
FieldDecision Sciences
TopicAuction Theory and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsLiabilityBusinessCommerceAdvertisingInternet privacyComputer scienceFinance
DOInot available

Abstract

fetched live from OpenAlex

The purpose o f this paper is to identijj; the importance o f a home country bias on the part o f consumers as a source of Liability of Foreignness by examining whether the coin grading services of well-known companies are worth more or less to auction participants depending upon the country of residence of the buyerandthehomecountryofthegradingcompany. WefindthatresidentsofCanadaplaceasignificantly greater premium on the services of the Canadian rating agency relative to U.S. rating agencies. However, U.S. residents alsofavor the Canadian rating agency. Hence, wefind no specific evidence ofa home country purchasing bias when purchasing Canadian silver dollars on eBay.

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)0.009
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.005
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.658
GPT teacher head0.673
Teacher spread0.015 · 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.

Study designBench or experimental
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

Citations1
Published2005
Admission routes1
Has abstractyes

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