Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
3Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5140549470
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Charity Varaidzo Katokwe (openalex, provider refresh)
- Charity Varaidzo Katokwe (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Human Ai Collab: 1 paper
- Labor Markets: 1 paper
Claim outcomes
- Turnover: 1 paper
Papers in the OpenAlex view
Latest stored OpenAlex author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| An XGBoost model trained on nine U.S. national workforce datasets predicts employee turnover with 92.8% accuracy, and SHAP analysis flags tenure, pay, age, benefits and local job openings as the dominant predictors; however, the study is predictive rather than causal and leaves key harmonization and validation details unreported.openalex | Charity Varaidzo Katokwe provider id |
2026-08-20 | 0 |
Citation observation summary
OpenAlex supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.