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Record W4411764203 · doi:10.1177/21582440251342506

Exploring the Landscape of American Sociology: A Bibliometric Analysis of Top Journal Publications (2011–2022)

2025· article· en· W4411764203 on OpenAlexaboutno aff
Zhou Jialin, Chaojin Wu

Bibliographic record

VenueSAGE Open · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicContemporary Sociological Theory and Practice
Canadian institutionsnot available
Fundersnot available
KeywordsSociologyBibliometricsSocial scienceRegional scienceLibrary scienceComputer science

Abstract

fetched live from OpenAlex

In the 21st century, American sociology has significantly influenced global sociological discourse. This research utilizes bibliometric methods to analyze 1,176 articles from the ASR, ARS, and AJS journals between 2011 and 2022, showcasing the dynamic development within the field. The research highlights the pivotal contributions of Harvard, Stanford, and UC Berkeley, and notes the emerging roles of the University of Toronto and NYU in shaping sociological discourse. We spotlight key scholars such as Soule, Desmond, Killewald, and Goldberg, whose works on social inequality—specifically in income, race, and gender—have significantly influenced contemporary sociology. the intensive focus on social inequality has spotlighted themes of income, race, and gender disparities. This focus complements the broad scholarly attention drawn to social movements, organizational studies, and the dynamics of social networks. Intersecting across these domains is the profound scholarly dedication to social justice. Indeed, the quest for equity and justice forms a critical undercurrent in contemporary sociological research, mirroring the discipline’s enduring engagement with these pivotal societal issues. This study not only charts the intellectual progression of American sociology but also underscores its global relevance and potential future directions. By integrating bibliometric analysis with a critical review of sociological literature, this article offers valuable insights into the evolving dynamics of the discipline.

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.

How this classification was reachedexpand

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)0.006
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.365
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.063
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.194
GPT teacher head0.420
Teacher spread0.226 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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