MétaCan
Menu
Back to cohort
Record W2071744688 · doi:10.4018/jiit.2005070104

A Model for Monitoring and Enforcing Online Auction Ethics

2005· article· en· W2071744688 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Intelligent Information Technologies · 2005
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCommon value auctionComputer scienceEnforcementForward auctionSet (abstract data type)Face (sociological concept)Information ethicsEthical codeAuction theoryArtificial intelligencePolitical scienceLawMicroeconomicsSociologyEconomics

Abstract

fetched live from OpenAlex

The online auction has become an important form of e-commerce. Although using a different mode for conducting auction activities, online auctions should abide by the same code of ethics outlined in the face-to-face auction environment. Yet, ethics-related issues for online auctions have not been fully discussed in the current literature. The unique features of online auctions present an opportunity to address how ethical conduct could be supported, monitored, and enforced in an online auction environment. With technology being the backbone of the online auction, information systems appear to be a useful tool in facilitating ethics enforcement. This article summarizes ethics-related issues that are particularly relevant in online auctions, and recommends a code of ethics that could be applied to online auctions. Based on this set of ethics, this article proposes a model for an information system that will support and enhance ethical conduct in an online auction environment.

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.863
Threshold uncertainty score0.331

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.046
GPT teacher head0.327
Teacher spread0.281 · 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