Aristocracy or Meritocracy? The Role of Elite Pedigree and Research Performance in New Accounting Faculty Placements
Bibliographic record
Abstract
ABSTRACT This paper investigates the joint and complex influences of elitism and merit in the hiring of new accounting faculty. Building on research showing that search committees value pedigree in hiring new faculty, we theorize both aristocratic (e.g., accessing or reinforcing elite networks) and meritocratic (e.g., signaling stronger future research potential) influences on the hiring of new accounting faculty. Using curriculum vitae from 381 Accounting Ph.D. Rookie Recruiting and Research Camps, we examine whether candidates graduating from elite accounting institutions place disproportionately higher than do their non-elite peers. Results suggest that elite pedigree predicts placement rank among candidates without favorable publication outcomes at top journals (e.g., acceptance or invitation to resubmit) but not among candidates with favorable publication outcomes. Favorable publication outcomes at other journals are unrelated to placement rank. The results suggest joint and complex aristocratic (elite-based) and meritocratic (productivity-based) influences in new accounting faculty hiring.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".