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Record W2130388807 · doi:10.1177/0011392110376026

Knowledge is not Power

2010· article· en· W2130388807 on OpenAlexaff
Zohreh Bayatrizi

Bibliographic record

VenueCurrent Sociology · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicContemporary Sociological Theory and Practice
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSociologyState (computer science)Sociological researchGovernment (linguistics)Competition (biology)Power (physics)Sociological theorySocial sciencePublic administrationPolitical science

Abstract

fetched live from OpenAlex

The discipline of sociology, as an officially funded area of research and teaching, was created in Iran five decades ago primarily as an instrument to help solve state problems. Today the state remains the main sponsor and client of sociological research. The absence of independent sources of research funding outside the government has left sociology dependent on state agencies and organizations. This situation has significant effects both on sociology and on the direction of policy-making in Iran. State-sponsored research is almost exclusively quantitative, narrowly problem-oriented, secretive and unable to offer concrete policy solutions. Lack of competition and the absence of non-governmental sources of funding have led to the marginalization of disinterested and fundamental research. Independent, critical sociology has always existed but it is in need of greater support. The situation in Iran is not unique: it reflects, in a magnified way, problems faced by sociologists in many other countries as they come under increasing pressures to undertake applied and policy-relevant research. The article concludes with five specific suggestions on how to improve the current state of sociological research.

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)0.011
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0100.087
Scholarly communication0.0190.031
Open science0.0020.012
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0190.006

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 head0.133
GPT teacher head0.451
Teacher spread0.318 · 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 designTheoretical or conceptual
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

Citations2
Published2010
Admission routes1
Has abstractyes

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