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Record W2038398651 · doi:10.1109/mm.2007.25

The High Cost of a Cheap Lesson

2007· article· en· W2038398651 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

VenueIEEE Micro · 2007
Typearticle
Languageen
FieldComputer Science
TopicOpen Source Software Innovations
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsGossipProduct (mathematics)Point (geometry)Computer scienceNew product developmentMarketingService (business)TelecommunicationsWorld Wide WebBusiness

Abstract

fetched live from OpenAlex

With a bit of effort, any technically skilled person can learn the latest information in their industry. That is so whether it concerns the design for a product, such as Apple's iPod, or involves demand for a newly deployed service, such as municipal Wi-Fi in a distant city. Although industry conferences, consulting reports, and trade magazines have always informed market participants, today these sources are supplemented by Web pages and community or industry forums. Any reasonably sized product market attracts an abundance of product reviewers and bloggers who track gossip about business initiatives and point out design flaws or triumphs. This article focuses on market experiment phenomenon: commodifying and accumulating lessons must go hand in hand. While that observation may sound excessively abstract, it is grounded in the experience of many markets

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.620
Threshold uncertainty score0.191

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.016
GPT teacher head0.273
Teacher spread0.256 · 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