Bridging Qualitative and Quantitative Methods in Organizational Research: Applications of Synthetic Control Methodology in the U.S. Automobile Industry
Why this work is in the frame
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Bibliographic record
Abstract
We assess the utility of synthetic control, a recently developed empirical methodology, for applications in organizational research. Synthetic control acts as a bridge between qualitative and quantitative research methods by enabling researchers to estimate treatment effects in contexts with small samples or few occurrences of a phenomenon or treatment event. The method constructs a counterfactual of a focal firm, or other observational unit, based on an objectively weighted combination of a small number of comparable but untreated firms. By comparing the firm’s actual performance to its counterfactual replica without treatment, synthetic control estimates, under certain assumptions, the magnitude and direction of treatment effects. We illustrate and critique the method in the context of the U.S. auto industry by estimating (a) the effect of government intervention in Chrysler’s management from 2009 to 2011 on its sales volumes and (b) the impact of Toyota’s 2010 “acceleration crisis” on Camry sales.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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