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Record W2175765694 · doi:10.19030/jbcs.v10i2.8501

Derek Szeto: RedFlagDeals.com A Case Study On When To Exit Your Successful Startup

2014· article· en· W2175765694 on OpenAlexaffabout
Sean Wise, Madelon Crothers

Bibliographic record

VenueJournal of Business Case Studies (JBCS) · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBusinessProcess managementMarketingBusiness administrationOperations managementIndustrial organizationEconomics

Abstract

fetched live from OpenAlex

RedFlagDeals.com (RFD) is a daily virtual destination for over 450,000 Canadian consumers seeking and sharing coupons, promotions, and deals. Founder, Derek Szeto, created the website because he saw an unmet need in the Canadian market and did not hesitate to create something that is ten times better than spending hours each week clipping coupons on his kitchen counter. From the beginning, Derek and his team bootstrapped their way to the top. He built the business out of nothing in his parents’ basement, with zero outside capital. He and his two employees funded the project themselves, without any external assistance. It was not until they had reached over a two million unique visitors that outside investors approached them to offer expansion funding (Naked Entrepreneur, 2013). Notwithstanding, Derek declined all offers of investment from outsiders. February 2010 marked RFD’s most rewarding milestone to date. Derek made the decision that he would sell to Yellow Pages Group Canada. After the transaction, Derek moved over to work under Yellow Pages Group Canada (YPGC) in the position of General Manager-Deals, Coupons, and Shopping. In late 2011, RFD was one of the 100 most popular websites in the country (Szeto, 2011). To this day, RFD attracts more than 2.5 million monthly readers by providing coupons and promotions across 14 categories (RedFlagDeals.com, 2013).

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
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 head0.065
GPT teacher head0.309
Teacher spread0.244 · 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 designCase report
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

Citations0
Published2014
Admission routes2
Has abstractyes

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