{"id":"https://openalex.org/W7225100604","doi":"https://doi.org/10.31234/osf.io/259gt_v1","title":"llmClassificR: Text Classification with Large Language Models, Network Psychometrics, and Calibrated Uncertainty in R","display_name":"llmClassificR: Text Classification with Large Language Models, Network Psychometrics, and Calibrated Uncertainty in R","publication_year":2026,"publication_date":"2026-11-02","ids":{"openalex":"https://openalex.org/W7225100604","doi":"https://doi.org/10.31234/osf.io/259gt_v1"},"language":"en","primary_location":{"id":"doi:10.31234/osf.io/259gt_v1","doi":"https://doi.org/10.31234/osf.io/259gt_v1","is_oa":true,"landing_page_url":"https://doi.org/10.31234/osf.io/259gt_v1","pdf_url":null,"source":null,"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"volume":null,"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.31234/osf.io/259gt_v1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043910258","display_name":"Hudson Fernandes Golino","orcid":"https://orcid.org/0000-0002-1601-1447","observed_orcids":["https://orcid.org/0000-0002-1601-1447","https://orcid.org/0000-0002-9798-7037"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Hudson Golino","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5043910258"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.5228999853134155,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T13910","display_name":"Computational and Text Analysis Methods","score":0.5228999853134155,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.399399995803833,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11550","display_name":"Text and Document Classification Technologies","score":0.020500000566244125,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/text-classification","display_name":"text classification","score":0.9850000143051147},{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.996999979019165},{"id":"https://openalex.org/keywords/network-psychometrics","display_name":"network psychometrics","score":0.9210000038146973},{"id":"https://openalex.org/keywords/multiplex-networks","display_name":"multiplex networks","score":0.6159999966621399},{"id":"https://openalex.org/keywords/transformer-layers","display_name":"transformer layers","score":0.6299999952316284},{"id":"https://openalex.org/keywords/unsupervised-text-classification","display_name":"unsupervised text classification","score":0.6729999780654907},{"id":"https://openalex.org/keywords/network-embedding","display_name":"graph embedding","score":0.5320000052452087},{"id":"https://openalex.org/keywords/self-consistency","display_name":"self-consistency","score":0.8489999771118164},{"id":"https://openalex.org/keywords/retrieval-augmented-generation","display_name":"retrieval-augmented generation","score":0.7599999904632568},{"id":"https://openalex.org/keywords/topic-detection","display_name":"topic extraction","score":0.8450000286102295},{"id":"https://openalex.org/keywords/jev","display_name":"Jev","score":0.6690000295639038},{"id":"https://openalex.org/keywords/uncertainty-calibration","display_name":"uncertainty calibration","score":0.6190000176429749},{"id":"https://openalex.org/keywords/conformal-prediction","display_name":"conformal prediction","score":0.8240000009536743},{"id":"https://openalex.org/keywords/temperature-scaling","display_name":"temperature scaling","score":0.7670000195503235},{"id":"https://openalex.org/keywords/label-reliability","display_name":"label reliability","score":0.5899999737739563},{"id":"https://openalex.org/keywords/goemotions","display_name":"GoEmotions","score":0.6629999876022339}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7235999703407288},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5796999931335449},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5710999965667725},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5206999778747559},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4925999939441681},{"id":"https://openalex.org/C2780416260","wikidata":"https://www.wikidata.org/wiki/Q2063","display_name":"JSON","level":2,"score":0.489300012588501},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4595000147819519},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4560000002384186},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4269999861717224},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4023999869823456},{"id":"https://openalex.org/C52146309","wikidata":"https://www.wikidata.org/wiki/Q7431116","display_name":"Schema (genetic algorithms)","level":2,"score":0.39469999074935913},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3781999945640564},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.35370001196861267},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3287999927997589},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.3061999976634979},{"id":"https://openalex.org/C170133592","wikidata":"https://www.wikidata.org/wiki/Q1806883","display_name":"Latent semantic analysis","level":2,"score":0.3021000027656555},{"id":"https://openalex.org/C155092808","wikidata":"https://www.wikidata.org/wiki/Q182557","display_name":"Computational linguistics","level":2,"score":0.2939999997615814},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2937000095844269},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2879999876022339},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2815000116825104},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2718999981880188},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.26460000872612},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.2556000053882599},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.31234/osf.io/259gt_v1","doi":"https://doi.org/10.31234/osf.io/259gt_v1","is_oa":true,"landing_page_url":"https://doi.org/10.31234/osf.io/259gt_v1","pdf_url":null,"source":null,"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},{"id":"pmh:oai:share.osf.io:cab5c52b-925e-46fc-b243-c29476738de9","doi":null,"is_oa":true,"landing_page_url":"https://osf.io/259gt","pdf_url":null,"source":{"id":"https://openalex.org/S4306401687","display_name":"PsyArXiv (OSF Preprints)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I2799848540","host_organization_name":"Center for Open Science","host_organization_lineage":["https://openalex.org/I2799848540"],"host_organization_lineage_names":[],"type":"repository"},"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.31234/osf.io/259gt_v1","doi":"https://doi.org/10.31234/osf.io/259gt_v1","is_oa":true,"landing_page_url":"https://doi.org/10.31234/osf.io/259gt_v1","pdf_url":null,"source":null,"license":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W6959789017","https://openalex.org/W7100950619","https://openalex.org/W4407828806","https://openalex.org/W3109245223","https://openalex.org/W6959993114","https://openalex.org/W4408966935","https://openalex.org/W4389520455","https://openalex.org/W7216204868","https://openalex.org/W6885149959","https://openalex.org/W2948971334"],"abstract_inverted_index":{"Text":[0],"classification":[1,42,95,135,145,170],"has":[2],"become":[3],"a":[4,54,70,116,129],"central":[5],"measurement":[6],"problem":[7],"in":[8,69],"psychology":[9],"and":[10,14,31,74,86,112,159,164,174,185,190,199,223,264],"the":[11,15,23,60,104,121,217,251,254,257],"social":[12],"sciences,":[13],"arrival":[16],"of":[17,25,96,106,212,253],"large":[18],"language":[19],"models":[20,37,43],"(LLMs)":[21],"multiplied":[22],"number":[24],"available":[26],"strategies,":[27],"from":[28,124,182],"contextual":[29],"encoders":[30],"embedding":[32,113],"services":[33],"to":[34,103,235,269],"prompted":[35,144],"generative":[36,147],"and,":[38],"more":[39],"recently,":[40],"general-purpose":[41],"such":[44],"as":[45],"Jev,":[46],"which":[47,64,231],"return":[48],"probabilities":[49],"for":[50],"typed":[51],"questions":[52],"about":[53],"text.":[55],"The":[56,90,203],"present":[57],"paper":[58],"introduces":[59],"R":[61],"package":[62,91],"llmClassificR,":[63],"brings":[65],"these":[66],"strategies":[67],"together":[68],"single,":[71],"consistent":[72],"interface,":[73],"shows":[75],"how":[76,237,250,265],"they":[77],"can":[78,262],"be":[79],"combined":[80],"with":[81,98,146,149,171],"network":[82],"psychometrics,":[83],"complexity":[84],"science,":[85],"modern":[87],"uncertainty":[88,267],"quantification.":[89],"implements":[92],"(a)":[93],"unsupervised":[94],"texts":[97,207],"exploratory":[99],"graph":[100],"analysis":[101],"applied":[102],"embeddings":[105],"local":[107,186],"encoder":[108],"models,":[109],"decoder":[110],"LLMs,":[111,148,210],"APIs,":[114],"including":[115],"prime-weight":[117],"encoding":[118],"that":[119,239,259],"combines":[120],"networks":[122],"estimated":[123],"every":[125],"transformer":[126],"layer":[127],"into":[128],"single":[130],"multiplex":[131],"representation;":[132],"(b)":[133],"supervised":[134],"heads":[136],"on":[137,243,247],"frozen":[138],"embeddings,":[139],"evaluated":[140],"by":[141,209,216,227],"cross-validation;":[142],"(c)":[143],"shuffled":[150],"batching,":[151],"label":[152,183],"validation,":[153],"strict":[154],"JSON":[155],"Schema":[156],"outputs,":[157],"ensembles,":[158],"self-consistency;":[160],"(d)":[161],"retrieval-augmented,":[162],"automated,":[163],"human-guided":[165],"topic":[166],"discovery;":[167],"(e)":[168],"Jev-based":[169],"Choice,":[172],"Noul,":[173],"Score":[175],"questions;":[176],"(f)":[177],"do-it-yourself":[178],"Jev-like":[179],"classifiers":[180],"built":[181],"descriptions":[184,211],"or":[187],"API":[188],"embeddings;":[189],"(g)":[191],"calibration,":[192],"temperature":[193],"scaling,":[194,196],"Platt":[195],"selective":[197],"classification,":[198],"conformal":[200],"prediction":[201],"sets.":[202],"tutorial":[204],"uses":[205],"synthetic":[206,244],"generated":[208],"emotional":[213],"situations":[214],"labeled":[215,226],"people":[218],"who":[219],"wrote":[220],"them":[221],"(EmpatheticDialogues),":[222],"Reddit":[224],"comments":[225],"several":[228],"raters":[229],"(GoEmotions),":[230],"makes":[232],"it":[233],"possible":[234],"show":[236],"methods":[238],"look":[240],"nearly":[241],"perfect":[242],"data":[245],"behave":[246],"real":[248],"text,":[249],"reliability":[252],"labels":[255],"limits":[256],"accuracy":[258],"any":[260],"classifier":[261],"demonstrate,":[263],"model":[266],"relates":[268],"human":[270],"disagreement.":[271]},"counts_by_year":[],"updated_date":"2026-11-05T12:25:35.769452","created_date":"2026-11-03T00:00:00"}
