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Record W1989919661 · doi:10.5195/tlp.2008.42

Mineshafts on Treasure Island: A Relief Map of the eBay Fraud Landscape

2008· article· en· W1989919661 on OpenAlexaff
Mary M. Calkins, Alexei N. Nikitkov, Vernon J. Richardson

Bibliographic record

VenuePittsburgh Journal of Technology Law and Policy · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicLaw, logistics, and international trade
Canadian institutionsBrock University
Fundersnot available
KeywordsTreasureLiberian dollarWeb siteThe InternetAdvertisingBusinessCommerceInternet privacyArtHistoryWorld Wide WebComputer scienceFinanceArchaeology

Abstract

fetched live from OpenAlex

As explorers once opened up new trade passages, thus attracting hordes of both honest traders and dishonest pirates, so too has the Internet opened new lanes of commerce and attracted the modern versions of the same. One of the widest of these lanes undoubtedly runs through eBay, located at http://www.ebay.com . From its humble beginnings as a little-known auction site hawking PEZ candy dispensers, broken laser pointers and other garage-sale pickings, eBay has transformed itself into a reputable public sales powerhouse where a Gulfstream II jet, million-dollar sports artifacts, and Madonna’s wedding tiara might easily change hands.4

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0060.002
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0330.003

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 head0.015
GPT teacher head0.231
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 designNot applicable
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

Citations8
Published2008
Admission routes1
Has abstractyes

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