Entrepreneurship Bias and the Mass Media: Evidence from Big Data
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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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 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".