Bibliographic record
Abstract
The U.S. labor market is dynamic and complex, and understanding wage data across different segments of the workforce is critical to providing policymakers and business leaders with actionable insights. There is no labor index that assesses the labor market performance at such a detailed level as the ADP Research Institute's Workforce Vitality Report (WVR). Drawing on the actual, aggregated and anonymous payroll data of 24 million Americans paid by ADP, the WVR looks at key dynamics and market indicators including wage growth, hours worked and turnover rate. Unlike other data sets, the WVR calculates wage growth of individual workers on a quarter-to-quarter basis, avoiding the deviations caused by various workplace occurrences, like when new workers are hired and older ones retire. In this paper, Dr. Ahu Yildirmaz, head of the ADP Research Institute, drills down into wage growth by industry, age, gender and income level, as well as for both job holders and job switchers. Using WVR data, Ahu walks through those factors contributing to overall shifts in wage growth, the future of the labor market and what this data means for today's U.S. workforce.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".