Paper Citation Record · LEDGER

Distinguishing Scams and Fraud with Ensemble Learning

As of 19 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 1 inbound Pith citation observation for arXiv:2412.08680.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.08680 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T17:47:39.460596Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T00:09:34.115311Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

27 of 27 outbound references displayed

verified exact1
verified fuzzy22
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

2
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

Observation c55267fa-0a50-4b8e-8bfa-10ac026f8a4b · outbound

This paper cites Student loans and financial distress: A qualitative analysis of the most common student loan complaints.

Distinguishing Scams and Fraud with Ensemble Learning Student loans and financial distress: A qualitative analysis of the most common student loan complaints

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.679141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 359823a2-2c41-4df5-a4b0-34f3e6b04ccb · outbound

This paper cites Consumer response annual report, january 1 — december 31, 2023.

Distinguishing Scams and Fraud with Ensemble Learning Consumer response annual report, january 1 — december 31, 2023

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.671610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3058e8f1-a155-4055-a1cb-d5718d551842 · outbound

This paper cites Extracting training data from large language models.

Distinguishing Scams and Fraud with Ensemble Learning Extracting training data from large language models

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.664119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f402f37c-b6e6-40ca-ae18-cb53a61959e3 · outbound

This paper cites Consumer complaints database.

Distinguishing Scams and Fraud with Ensemble Learning Consumer complaints database

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.656649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 87a6b7d9-a11d-4989-8d04-2f8e2c0140af · outbound

This paper cites The science of persuasion.

Distinguishing Scams and Fraud with Ensemble Learning The science of persuasion

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.649116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5b3cec37-2067-4717-a4da-71782a0c4bcd · outbound

This paper cites As nationwide fraud losses top $10 billion in 2023, ftc steps up efforts to protect the public.

Distinguishing Scams and Fraud with Ensemble Learning As nationwide fraud losses top $10 billion in 2023, ftc steps up efforts to protect the public

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.641762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7208d70d-af70-47cf-98c8-2217ecd97a98 · outbound

This paper cites Financial exploitation vulnerability and social isolation in older adults: Results from a longitudinal survey.

Distinguishing Scams and Fraud with Ensemble Learning Financial exploitation vulnerability and social isolation in older adults: Results from a longitudinal survey

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.633984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 12e264cd-1166-4a5b-a1d7-8ae8f03a2f87 · outbound

This paper cites AnnoLLM: Making large language models to be better crowdsourced annotators.

Distinguishing Scams and Fraud with Ensemble Learning AnnoLLM: Making large language models to be better crowdsourced annotators

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.627409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9dde4e7d-9d8a-40d2-89c1-6dd210024180 · outbound

This paper cites John, Erin Picone-Decaro, Richard A Jenkins, and James W Carey.

Distinguishing Scams and Fraud with Ensemble Learning John, Erin Picone-Decaro, Richard A Jenkins, and James W Carey

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.620503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 75ca6110-721d-432f-868a-e9023a44f88f · outbound

This paper cites $1.8 billion going to victims of credit repair scam.

Distinguishing Scams and Fraud with Ensemble Learning $1.8 billion going to victims of credit repair scam

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.613297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ca75fccd-acbb-4bfb-96ea-cecc535c0f4e · outbound

This paper cites Scam compliance and the psychology of persuasion.

Distinguishing Scams and Fraud with Ensemble Learning Scam compliance and the psychology of persuasion

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.606319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8408c06b-1034-4b85-9067-340e6400ddac · outbound

This paper cites GPT-4 Model Card.

Distinguishing Scams and Fraud with Ensemble Learning GPT-4 Model Card

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.599525Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1db245c7-a990-4811-9bba-9a7e0ee5c980 · outbound

This paper cites Mortgage financing frauds in the us: An analysis of CFPB complaint database.

Distinguishing Scams and Fraud with Ensemble Learning Mortgage financing frauds in the us: An analysis of CFPB complaint database

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.592427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b801981f-ca2f-427e-b2ff-326bc0f463ab · outbound

This paper cites Redmiles, Amelia R.

Distinguishing Scams and Fraud with Ensemble Learning Redmiles, Amelia R

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.584686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ecdaa583-0728-4477-acfd-f45ef9c58949 · outbound

This paper cites How to level up your fraud defense with chatgpt’s own ai sugges- tions.

Distinguishing Scams and Fraud with Ensemble Learning How to level up your fraud defense with chatgpt’s own ai sugges- tions

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.576232Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 613d83c5-da3e-4168-a1ca-f7feb7484440 · outbound

This paper cites A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications.

Distinguishing Scams and Fraud with Ensemble Learning A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T17:47:39.431633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd5be44d-ee17-463f-b001-5fcd011cd5b5 · outbound

This paper cites Using chatgpt for real-time fraud detection in finance.

Distinguishing Scams and Fraud with Ensemble Learning Using chatgpt for real-time fraud detection in finance

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.568315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 875eff3a-f34a-420e-896a-0cbdc4c1240c · outbound

This paper cites Can LLMs be Scammed? A Baseline Measurement Study.

Distinguishing Scams and Fraud with Ensemble Learning Can LLMs be Scammed? A Baseline Measurement Study

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-11T17:47:39.502523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4ad0c16a-f4df-4b0c-a9e5-7f3c160c2a51 · outbound

This paper cites Online-dating romance scam in malaysia: An analysis of online conversations between scammers and victims.

Distinguishing Scams and Fraud with Ensemble Learning Online-dating romance scam in malaysia: An analysis of online conversations between scammers and victims

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.560396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9401d5c9-8857-4fdf-b7b6-8aa9f1edacde · outbound

This paper cites Combating Phone Scams with LLM-based Detection: Where Do We Stand?.

Distinguishing Scams and Fraud with Ensemble Learning Combating Phone Scams with LLM-based Detection: Where Do We Stand?

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T17:47:39.442798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b1551a7-a574-4f21-88c5-7fe7b61b83ae · outbound

This paper cites Large Language Models for Data Annotation and Synthesis: A Survey.

Distinguishing Scams and Fraud with Ensemble Learning Large Language Models for Data Annotation and Synthesis: A Survey

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T17:47:39.445813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ead3b766-4f58-4e83-84ee-3c143b420673 · outbound

This paper cites OFFICE OF CONSUMER RE- SPONSE.

Distinguishing Scams and Fraud with Ensemble Learning OFFICE OF CONSUMER RE- SPONSE

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.552066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ff32f7ee-c5ac-4e39-bedb-0895a5d2c6a8 · outbound

This paper cites How experts detect phishing scam emails.

Distinguishing Scams and Fraud with Ensemble Learning How experts detect phishing scam emails

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T17:47:39.451199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07004957-9689-4bf3-9801-125787794413 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Distinguishing Scams and Fraud with Ensemble Learning Chain-of-thought prompting elicits reasoning in large language models

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.540253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5f979d4f-c136-4b2e-8fdd-29c23bcbaaaa · outbound

This paper cites How to use norton’s free ai-powered scam detector.

Distinguishing Scams and Fraud with Ensemble Learning How to use norton’s free ai-powered scam detector

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.532174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 515ec918-e951-4090-a940-eaf3e775352b · outbound

This paper cites Vulnerability to fraud among chinese older adults: Do personality traits and loneliness matter? Journal of Elder Abuse & Neglect , 32(1):46–59, 2020.

Distinguishing Scams and Fraud with Ensemble Learning Vulnerability to fraud among chinese older adults: Do personality traits and loneliness matter? Journal of Elder Abuse & Neglect , 32(1):46–59, 2020

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.524488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 46546b90-de19-4054-a2dd-470405cbf37b · outbound

This paper cites it’s a fair game.

Distinguishing Scams and Fraud with Ensemble Learning it’s a fair game

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T17:47:39.517011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation ec87f48a-1639-49ae-862e-69b22e22fb94 · inbound

"It Warned Me Just at the Right Moment": Exploring LLM-based Real-time Detection of Phone Scams cites this paper.

"It Warned Me Just at the Right Moment": Exploring LLM-based Real-time Detection of Phone Scams Distinguishing Scams and Fraud with Ensemble Learning

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-09T00:09:34.711156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.