Paper Citation Record · LEDGER

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation

As of 19 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2505.04643.

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

pith.paper-citation-record.v1
2505.04643 v1

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Reference resolution

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Outbound references

Observation 8b736b59-6dca-4db3-9d52-6cd084d6099d · outbound

This paper cites John Wiley & Sons, 3 edition, 1977.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation John Wiley & Sons, 3 edition, 1977

Reference 1

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This paper cites On the theory of sampling from finite populations.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation On the theory of sampling from finite populations

Reference 2

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This paper cites A generalization of sampling without replacement from a finite universe.Journal of the American Statistical Association, 47(260):663–685, 1952.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation A generalization of sampling without replacement from a finite universe.Journal of the American Statistical Association, 47(260):663–685, 1952

Reference 3

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This paper cites Model assisted survey sampling.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Model assisted survey sampling

Reference 4

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This paper cites A Horvitz–Thompson-type estimator of species richness.Environmetrics, 22(7):901–910, 2011.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation A Horvitz–Thompson-type estimator of species richness.Environmetrics, 22(7):901–910, 2011

Reference 5

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Observation 6403fe85-b86b-4e4b-ac1d-9e0c1e6a2ece · outbound

This paper cites Estimating population treatment effects from a survey subsample.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Estimating population treatment effects from a survey subsample

Reference 6

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This paper cites High- resolution population estimation using household survey data and building footprints.Nature communications, 13(1):1330, 2022.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation High- resolution population estimation using household survey data and building footprints.Nature communications, 13(1):1330, 2022

Reference 7

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This paper cites Some Thoughts on Official Statistics and its Future (with discus- sion).

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Some Thoughts on Official Statistics and its Future (with discus- sion)

Reference 8

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This paper cites Statistics Canada’s Quality Assurance Framework.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Statistics Canada’s Quality Assurance Framework

Reference 9

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This paper cites Lag om den officiella statistiken [Offi- cial Statistics Act].

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Lag om den officiella statistiken [Offi- cial Statistics Act]

Reference 10

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This paper cites Regulation (EC) No 223/2009 of the European Parliament and of the Council of 11 March 2009 on European Statistics, 2009.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Regulation (EC) No 223/2009 of the European Parliament and of the Council of 11 March 2009 on European Statistics, 2009

Reference 11

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This paper cites The National Academies Press, Washington, DC, 2024.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation The National Academies Press, Washington, DC, 2024

Reference 12

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This paper cites Politics as Usual? Measuring Populism, Nationalism, and Authoritarianism in US Presidential Campaigns (1952–2020) with Neural Language Models.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Politics as Usual? Measuring Populism, Nationalism, and Authoritarianism in US Presidential Campaigns (1952–2020) with Neural Language Models

Reference 13

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This paper cites The augmented social scientist: Using sequential transfer learning to annotate millions of texts with human-level accuracy.Sociological Methods & Research, 53(3):1167–1200, 2024.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation The augmented social scientist: Using sequential transfer learning to annotate millions of texts with human-level accuracy.Sociological Methods & Research, 53(3):1167–1200, 2024

Reference 14

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This paper cites A deep language model for symptom extraction from clinical text and its application to extract COVID-19 symptoms from social media.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation A deep language model for symptom extraction from clinical text and its application to extract COVID-19 symptoms from social media

Reference 15

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This paper cites Emergingindustry classification based on BERT model.Information Systems, 128:102484, 2025.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Emergingindustry classification based on BERT model.Information Systems, 128:102484, 2025

Reference 16

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This paper cites Innovations in public health surveillance: An overview of novel use of data and analytic methods.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Innovations in public health surveillance: An overview of novel use of data and analytic methods

Reference 17

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This paper cites Anmälda brott 2023 slutlig statistik [Reported Crimes 2023 Final Statistics].https://bra.se/download/18.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Anmälda brott 2023 slutlig statistik [Reported Crimes 2023 Final Statistics].https://bra.se/download/18

Reference 18

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This paper cites Active Learning Literature Survey.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Active Learning Literature Survey

Reference 19

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This paper cites Prediction-powered inference.Science, 382(6671):669–674, 2023.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Prediction-powered inference.Science, 382(6671):669–674, 2023

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Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Natural language processing

Reference 23

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Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation A survey on sentiment analysis methods, applications, and challenges

Reference 24

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This paper cites Analytical mapping of opinion mining and sentiment analysis research during 2000–2015.Information Processing & Manage- ment, 53(1):122–150, 2017.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Analytical mapping of opinion mining and sentiment analysis research during 2000–2015.Information Processing & Manage- ment, 53(1):122–150, 2017

Reference 25

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This paper cites Text classification algorithms: A survey.Information, 10(4):150, 2019.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Text classification algorithms: A survey.Information, 10(4):150, 2019

Reference 26

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This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Attention is all you need.Advances in neural information processing systems, 30, 2017

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Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 29

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Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation SciBERT: A Pretrained Language Model for Scientific Text

Reference 30

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This paper cites A survey on text classification algorithms: From text to predictions.Information, 13(2):83, 2022.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation A survey on text classification algorithms: From text to predictions.Information, 13(2):83, 2022

Reference 32

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This paper cites Chatgpt for Text Annotation? Mind the Hype!SocArXiv preprint, 2023.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Chatgpt for Text Annotation? Mind the Hype!SocArXiv preprint, 2023

Reference 33

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This paper cites A survey of text classification with transformers: How wide? how large? how long? how accurate? how expensive? how safe? IEEE Access, 12:6518–6531, 2024.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation A survey of text classification with transformers: How wide? how large? how long? how accurate? how expensive? how safe? IEEE Access, 12:6518–6531, 2024

Reference 34

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This paper cites Are chatbots reliable text annotators? sometimes.PNAS nexus, 4(4):pgaf069, 2025.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Are chatbots reliable text annotators? sometimes.PNAS nexus, 4(4):pgaf069, 2025

Reference 35

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This paper cites From words to watts: Benchmarking the energy costs of large language model inference.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation From words to watts: Benchmarking the energy costs of large language model inference

Reference 36

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Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Machine Learning in Official Statistics

Reference 37

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Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Model-assisted survey regression estimation with the lasso

Reference 38

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This paper cites Brottsbalken [Swedish Penal Code], 1962.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Brottsbalken [Swedish Penal Code], 1962

Reference 39

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Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Reference we need to fix

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Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Playing with Words at the National Library of Sweden -- Making a Swedish BERT

Reference 42

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Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Don't Stop Pretraining: Adapt Language Models to Domains and Tasks

Reference 43

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This paper cites Learning from positive and unlabeled data: A survey.Machine Learning, 109(4):719–760, 2020.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Learning from positive and unlabeled data: A survey.Machine Learning, 109(4):719–760, 2020

Reference 44

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Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Decoupled Weight Decay Regularization

Reference 45

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Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Decoupled Weight Decay Regularization

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Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Deep Learning

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Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation LEGAL-BERT: The Muppets straight out of Law School

Reference 48

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This paper cites Downstream task performance of BERT models pre-trained using automatically de-identified clinical data.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Downstream task performance of BERT models pre-trained using automatically de-identified clinical data

Reference 49

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This paper cites Should You Mask 15% in Masked Language Modeling?.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Should You Mask 15% in Masked Language Modeling?

Reference 50

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This paper cites Spanbert: Improving pre-training by representing and predicting spans.Transactions of the association for computational linguistics, 8:64–77, 2020.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Spanbert: Improving pre-training by representing and predicting spans.Transactions of the association for computational linguistics, 8:64–77, 2020

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-16T00:50:20.370180Z

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This paper cites ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Reference 52

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Observation a6fba7ed-d06c-417c-87f9-a6c99344195a · outbound

This paper cites PMI-Masking: Principled masking of correlated spans.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation PMI-Masking: Principled masking of correlated spans

Reference 53

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This paper cites How to Train BERT with an Academic Budget.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation How to Train BERT with an Academic Budget

Reference 54

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This paper cites Language models are few-shot learners.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation Language models are few-shot learners

Reference 55

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Observation f2d39156-42fe-4fae-a7be-3c77a2db19c7 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation LLaMA: Open and Efficient Foundation Language Models

Reference 56

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Observation 9f799dd6-a32e-48a9-ae47-383eb9f03de0 · outbound

This paper cites not hate crime.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation not hate crime

Reference 57

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Pith citing papers

Observation 2391ebf9-069b-40c6-a9da-b9e92fc5a14b · inbound

Design-Based Prediction-Powered Inference for Spatial Data cites this paper.

Design-Based Prediction-Powered Inference for Spatial Data Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-08-15T14:29:53.900452Z

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