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Record W2140566743 · doi:10.1177/0002716207303581

Secrets of Gazelles: The Differences between High-Growth and Low-Growth Business Owned by African American Entrepreneurs

2007· article· en· W2140566743 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Annals of the American Academy of Political and Social Science · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)BusinessGovernment (linguistics)Demographic economicsEconomicsGeography

Abstract

fetched live from OpenAlex

The research findings are based on a national survey of 350 African American business owners whose companies had ten to one hundred employees. Each quarter of 2002 and 2003, owners were randomly selected and interviewed. Companies were classified into three groups according to their annual employment growth over five years: gazelles (20 percent or greater rate of growth), growth-oriented firms (1 to 19 percent), and no-growth firms (less than 1 percent or negative). In comparison to no-growth firms, gazelles were more likely to market to the government sector, less likely to compete on the basis of price, more likely to serve regional and national markets, and more likely to have fewer African Americans workers. CEOs of no-growth companies were more likely to have entered business because they lost a previous job. Surprisingly, no statistically significant differences appeared in thirty-nine other variables that defined owner attributes, firm characteristics, and business strategies of gazelles and no-growth firms.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.013
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 head0.054
GPT teacher head0.303
Teacher spread0.249 · 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