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Record W3088747997 · doi:10.1111/1911-3838.12233

The Competitive Intelligence Unit at Deloitte

2020· article· en· W3088747997 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueAccounting Perspectives · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCompetitive and Knowledge Intelligence
Canadian institutionsBrock University
Fundersnot available
KeywordsCompetitive intelligenceCompetitive advantageCapstoneUnit (ring theory)BusinessAccountingMarketingComputer sciencePsychology

Abstract

fetched live from OpenAlex

ABSTRACT The goal of competitive intelligence is to acquire information that can be used to make strategic decisions that further the objectives of the firm. Using publicly available information, this case describes the Competitive Intelligence Unit at the Big 4 accounting firm Deloitte and the techniques that the unit used to obtain strategic information about the plight of the consulting firm BearingPoint, a firm that Deloitte subsequently purchased in March 2009. This case can be used in an advanced undergraduate accounting theory, assurance, or capstone accounting course. It raises several interesting questions: What are acceptable and unacceptable competitive intelligence gathering techniques? Does competitive intelligence gathering by accounting firms serve the public interest?

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 categoriesInsufficient payload (model declined to judge)
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 score0.931
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.005

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 head0.035
GPT teacher head0.254
Teacher spread0.219 · 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