The Importance of Client Size in the Estimation of the Big 4 Effect: A Comment on DeFond, Erkens, and Zhang (2016)
Bibliographic record
Abstract
DeFond, Erkens, and Zhang (2016, hereafter DEZ) provide comprehensive analyses highlighting how random variations in propensity score matching (PSM) design choices affect inferences concerning the existence of the Big 4 auditor effect. The conclusion of DEZ is that Lawrence, Minutti-Meza, and Zhang (2011, hereafter LMZ) fail to find a Big 4 effect because of PSM’s sensitivity to design choices. We believe that DEZ emphasizes the need to think carefully when implementing PSM. We offer our views on DEZ findings and suggestions for future 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 | 0.091 | 0.289 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.028 | 0.040 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".