MétaCan
Menu
Back to cohort
Record W4379054549 · doi:10.1111/joes.12571

Thirty years of academic finance

2023· article· en· W4379054549 on OpenAlex
David Ardia, Keven Bluteau, Mohammad‐Abbas Meghani

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Economic Surveys · 2023
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversité de SherbrookeGroup for Research in Decision AnalysisHEC Montréal
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of California, Los AngelesPeking UniversityUniversità BocconiHong Kong University of Science and TechnologyTsinghua UniversityUniversity of MelbourneUniversity of SydneySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungUniversity of ManchesterNanyang Technological UniversityNational University of SingaporeLondon School of Economics and Political ScienceUniversity of New South WalesInstitut de Valorisation des DonnéesYork UniversityUniversity of PennsylvaniaUniversity of OxfordYale University
KeywordsEconomics

Abstract

fetched live from OpenAlex

Abstract We study how the financial literature has evolved in scale, research team composition, and article topicality across finance‐focused academic journals from 1992 to 2021. We document that the field has vastly expanded regarding outlets and published articles. Teams have become larger, and the proportion of women participating in research has increased significantly. Using the Structural Topic Model, we identify 45 topics discussed in the literature. We investigate the topic coverage of individual journals and can identify highly specialized and generalist outlets, but our analyses reveal that most journals have covered more topics over time, thus becoming more generalist. Finally, we find that articles with at least one woman author focus more on topics related to social and governance aspects of corporate finance. We also find that teams with at least one top‐tier institution scholar tend to focus more on theoretical aspects of finance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.099
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Bibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.416
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0990.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0290.038
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.613
GPT teacher head0.567
Teacher spread0.047 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it