Shifts in national entrepreneurial culture: The promise of linguistic cultural artifacts and machine learning analysis
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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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".