Shifts in national entrepreneurial culture: The promise of linguistic cultural artifacts and machine learning analysis
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Research Summary We develop a dynamic view of national entrepreneurial culture by examining the linguistic evolution of media‐produced cultural artifacts—entrepreneurship‐related newspaper articles. Applying machine learning to 690,088 articles from 103 newspapers across the United States between 1996 and 2016, we identify a growing positivity bias toward entrepreneurship at the national level evidenced by rising emotional tone and declining analytical thinking. This bias varies by topic, with “entrepreneurial aspirations and journeys” driving the trend. Our analyses also suggest this bias may encourage the creation of new ventures but limit venture growth potential. We highlight theoretical and methodological contributions to research on national entrepreneurial culture and identify promising avenues for future research. Managerial Summary We examine how a country's cultural attitudes toward entrepreneurship change over time by studying relevant newspaper articles. We also consider if any changes in such attitudes may have implications for the quantity and quality of a country's new ventures. After analyzing 690,088 articles from 103 newspapers across the United States between 1996 and 2016, we find a growing positivity bias toward entrepreneurship evidenced by increasing rates of positive tone and decreasing rates of analytical thinking. This bias is largest when media articles discuss entrepreneurial aspirations and journeys. Our analyses also suggest this bias may facilitate the creation of new ventures but limit their growth potential. These findings have implications for understanding and measuring national entrepreneurial culture, and create opportunities for future research.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it