Bridging Qualitative and Quantitative Methods in Organizational Research: Applications of Synthetic Control Methodology in the U.S. Automobile Industry
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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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 | 0.100 | 0.242 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".