Entrepreneurship Bias and the Mass Media: Evidence from Big Data
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.
Bibliographic record
Abstract
Does the mass media promote entrepreneurship? Using big data in combination with a machine learning-aided analysis, we discover a positive sentiment bias associated with entrepreneurship present in two major English-language media outlets: The New York Times and the Financial Times. Over 800,000 excerpts from 12- and 16-year periods were analyzed. Those containing the words “entrepreneur” and “founder” were found to have much more positive sentiment than did excerpts with the words “manager” and “executive.” A parallel analysis of the FANG companies (i.e., Facebook, Amazon, Netflix, and Google) in comparison to a set of older and more established Fortune 500 companies produced similar results. While more work is needed to verify the link between media biases and career choice, we believe this media bias promotes entrepreneurship, resulting in lower (average) incomes and higher risks for those engaged in this career path. However, because entrepreneurial activity can create positive externalities in the broader economy, this bias, while financially disadvantageous for the average entrepreneur, may be beneficial overall for society.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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