Noise, Noise Everywhere: Mixed Signals from Academic Promotion Decisions
Bibliographic record
Abstract
While academics need to achieve quality performance in research, teaching, and service to achieve an advancement in rank, many claim that the goalposts of achievement are unclear and subjective. At the same time, we know that institutions use the academic promotion process to achieve the long- and short-term strategic needs of the institution. It may be that the need to strategically deviate from initially communicated promotion policies has led academics to be presented with signal noise regarding what performance is needed to achieve an advancement in rank. As a result, this study explored how credible are the signals sent by higher education institutions via their academic promotion decisions for advancement to senior faculty ranks. To achieve this, we analyzed the research and service activities of 561 faculty from Ireland, the UK, the US, Canada, Australia, and New Zealand who were recently promoted to Senior Lecturer up to Professor. From our analysis, we found that the promotion process creates three levels of signal noise, overall signal noise (at the rank level), internal signal noise (at the institutional level), and external signal noise (at the regional level). These signal noises are created as institutions make continuously inconsistent promotion decisions for individuals being promoted to Senior Lecturer, Associate Professor, and Professor. These findings call into question assumptions academics have about academic careers (e.g., citations are an important metric for career advancement) and the career-related decisions we make when benchmarking against those who have gone before us as we find not all ranks are created equally.
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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 | 0.026 | 0.147 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".