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Record W4281813525 · doi:10.1177/13684310221099695

Castells versus Bell: A comparison of two grand theorists of the information age

2022· article· en· W4281813525 on OpenAlexfundno aff
Alistair S. Duff

Bibliographic record

VenueEuropean Journal of Social Theory · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicUniversity-Industry-Government Innovation Models
Canadian institutionsnot available
FundersCanadian Nuclear Safety CommissionUniversitat Oberta de CatalunyaHarvard University
KeywordsSociologyNormativeEpistemologyCapitalismInterpretation (philosophy)RestructuringSocial sciencePhilosophyLawPoliticsPolitical science

Abstract

fetched live from OpenAlex

Daniel Bell (1919–2011) and Manuel Castells (1942–) are the grand theorists of the information age. The article provides a detailed, up-to-date, comparative analysis of their writings. It begins with their methodologies, identifying numerous commonalities in their post-Marxian frameworks. The substance of their theories is then examined, where it is shown that both plausibly explain contemporary social reality in terms of the interplay of three forces: the information technology revolution, the restructuring of capitalism and the innovational role of culture. There are found to be major similarities in their accounts (the Kantian interpretation, social stratification) but also significant divergences (role of science, the fourth world, the normative content of culture). Suitably combined, Bell’s and Castells’s thought goes a long way towards delivering a persuasive sociological theory of the global information society. However, the article concludes by suggesting that extensive further work is needed to clarify the precise relationships between the three factors and their relative weightings in the equations required to explain recent social change.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.011
Science and technology studies0.0080.023
Scholarly communication0.0170.013
Open science0.0020.006
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0050.001

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 head0.032
GPT teacher head0.248
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2022
Admission routes1
Has abstractyes

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