MétaCan
Menu
Back to cohort
Record W3121327065 · doi:10.1111/eufm.12100

Due Diligence and Investee Performance

2016· article· en· W3121327065 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

VenueEuropean Financial Management · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsYork University
Fundersnot available
KeywordsDue diligenceEndogeneityContext (archaeology)Equity (law)ImperfectBusinessMatching (statistics)Value (mathematics)EconomicsEconometricsComputer scienceFinancePolitical scienceStatisticsMathematicsMachine learning

Abstract

fetched live from OpenAlex

Abstract We estimate the economic value of due diligence (DD) in the context of private equity by investigating the relationship between DD and investee performance, while controlling for endogeneity. With the adoption of a novel dataset, we find evidence highly consistent with the view that a thorough DD is associated with improved investee performance. We also distinguish the role of different types of DD and show that the DD performed by fund managers has a more pronounced impact on performance. Instead, the DD mainly performed by external agents, i.e., consultants, lawyers and accountants, gives rise to puzzling results and imperfect matching.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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 head0.012
GPT teacher head0.173
Teacher spread0.161 · 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