On the public citation graph

Using Quantifiable Behavioral Traits to Predict a Country's COVID19 Infection Rates

https://doi.org/10.13140/rg.2.2.20276.58240

0 signed · 2 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4762168openalex-extracted

Learning Through the Social Conditions Experienced by Orphans During #Covid-19 with Social Media Mining

inferred← citeshttps://doi.org/10.1108/jhass-07-2021-0128openalex-snapshot

Sociocultural behavioral traits in modelling the prediction of COVID-19 infection rates

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.