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Record W2167384224 · doi:10.1109/hicss.2006.195

Genres of Spam: Expectations and Deceptions

2006· article· en· W2167384224 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSpam and Phishing Detection
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceInternet privacySpammingComputer securityWorld Wide WebThe Internet

Abstract

fetched live from OpenAlex

This paper is a pilot study that explores how the concept of genre can be applied to the massive set of digital documents known as ‘spam’. The authors studied 300 spam messages collected over 15 weeks from a university email system. Messages were coded based on content, form and specific features as well as on the manifest relationship to existing genres of communication. The paper argues that spam is not a single genre but many genres. For the most part, the genres evoked in spam are adaptations of print to Internet, including information artifacts, pamphlets, business cards, order forms, bulletins, advertisements, and "Nigerian letters". With spam, however, the concept of genre operates at several levels. Often, there is a contradiction between the manifest genre and the underlying purposes. The paper concludes that spam exploits genre by conforming to known forms while at the same time breaching those norms.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.765
Threshold uncertainty score0.087

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.006
GPT teacher head0.204
Teacher spread0.198 · 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

Quick stats

Citations26
Published2006
Admission routes1
Has abstractyes

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