From the hash-bound evidence releases
Recorded claims
Each claim states what the inspected source says, at the recorded location—bounded by its scope and graded by its confidence. Nothing here is a synthesis across works.
The work uses machine learning to translate applicant work histories into predictors of performance and turnover.
Scope: title-level metadata; abstract not inspected
The title identifies a machine-learning approach that converts applicant work histories into predictors of job performance and turnover.
Scope: title-level
Structured relationships
In-corpus citations
Only source-supplied cites relationships whose two endpoints are admitted are shown. Invocation evidence remains a separate relationship.
Cites (0)
No in-corpus outgoing citation is available in the current enrichment coverage.
Cited by (5)
- Whither bias goes, I will go: An integrative, systematic review of algorithmic bias mitigation.
- Algorithmic Writing Assistance on Jobseekers’ Resumes Increases Hires
- Algorithmic management in the gig economy: A systematic review and research integration
- Does AI Cheapen Talk? Theory and Evidence from Global Entrepreneurship and Hiring
- Ghost in the Machine: On Organizational Theory in the Age of Machine Learning
Boundaries
Limitations & independence
Recorded at coding time, carried with the work forever. A claim without its limits is not evidence.
Recorded limitations
- Crossref supplied bibliographic metadata but no abstract.
- Scope and claim are limited to the title-level description.
- Crossref supplied only year and month, so published_date is null rather than an inferred day.
- Crossref title metadata only; an abstract was not present in the retained response and full text was not inspected.
- The publisher surface was not queried after the lane’s quarantined dynamic-challenge response; no independent publisher-page verification was attempted.
Source independence
publisher-only