Bibliographic record
Abstract
Absolute necessity entrepreneurs, 196 Absolutism, 183 Adult Population Survey (APS), 361, 379 Afghanistan business environment, 214 data and variables, 219-223 descriptive statistics, 216-217 gross domestic product (GDP), 214 individual level, 219-223 institutional level, 225-227 motivational factors, 215 organizational culture, 214 population, 214 psychological factors, 215 restaurant, 214-215 societal level, 223-225 Afghanistan women chamber of commerce and industry (AWCCI), 227 Africa, 229-230, 338 Aḥmad Shāh-e Qājār reign (1909-1925), 174-176 Amirkabir University of Technology, Global Entrepreneurship Monitor (GEM), 357 global financial crisis, 357 goodness-of-fit model, 364 government policies, 358 guanxi network, 114-115 male-dominated entrepreneurial community, 121-122 market-oriented economy, 112 mass entrepreneurship, 357-358 meso-level contextual influences, 111 national entrepreneurial development, 115-117 negotiations, women entrepreneurs vs. societal influences, 121 preentrepreneurial work experience, 113-115 regional culture, 118 social networks, 360 state-owned enterprises, 113 transitional economies, 358 Coding, 254-255 Communication Theory of Resilience, 122 Confirmatory factor analysis, 222 Constitutional Charter, 172-173 Constitutionalism, 173-174 Constitutional Revolution, 180-181 Content analysis, 254-256, 268, 281 Context dependency females' access, entrepreneurial capital, 376-377 institutional support, 377-378 Contextual effect, country status economic status, 62 environmental quality, 61 gender diversity variable, 62-63 marginal benefit, 63 Control variables, 380 Corporate entrepreneurship correlation variables, 131, 133-134 ease of doing business, 138 labor freedom predictive margins, 136, 138 logistic regressions, 133, 136 opportunity recognition predictive margins, 133, 136-137 perceived skills predictive margins, 133, 136-137 Corporate social responsibility, 52 COVID-19, 5, 7, 103, 106, 154, 310, 356 Creative destruction process, 76 Crowdfunding, 296, 327-328 Cultural context, 232 Cultural-driven triangulation, 184-186 Cultural factors, 234 Culture sensitivity, 223-224
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.787 | 0.836 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".