The Face of Fortune: A Review on How Machine Learning Can Address Limitations in Past Research
Bibliographic record
Abstract
In recent years, an emerging number of studies have linked CEO faces to an array of company outcomes. The current research connects with this burgeoning line of research by first providing a comprehensive review of past studies, and then categorizing studies according to their micro-theoretical foundations, namely the thin-slice literature, evolutionary psychology and facial perception. Drawing from psychological studies of human faces, we identified two major limitations in the existing CEO faces literature—the problem of small sample size and the lack of standardization of facial images. While these limitations tremendously hamper the replicability and generalizability of studies on CEO faces, we argue that recent advancements in machine learning can help researchers alleviate the limitations. We demonstrate in details how to apply these techniques and empirically show how they help to improve standardizations in facial images. We also provide an open dataset that is processed by machine-learning and that meets the standards in social psychology research, to facilitate future studies of CEO faces. We conclude by discussing how these machine learning techniques as well as this open dataset can contribute to the study of CEO faces and the upper echelons 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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".