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Record W2107719182

EMPOWERMENT: FROM NOISE TO VOICE

2010· article· en· W2107719182 on OpenAlex

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHuman Development Resource Network (HDRNet) · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsConcordia University
Fundersnot available
KeywordsEmpowermentWrightPoliticsPublic relationsCitizen journalismState (computer science)Corporate governanceSociologyPolitical scienceDemocracyPublic administrationManagementEconomicsEngineeringLaw
DOInot available

Abstract

fetched live from OpenAlex

This article on empowerment focuses on the capacity of citizens to create new institutional spaces in which they play a decisive role in designing local development strategies by forming strategic alliances with various social actors, including the state. Drawing on the concept of empowered participatory governance developed by Erik Olin Wright and Archon Fung, the author distinguishes between citizen engagement as processes of public consultation (noise) and comprehensive community economic initiatives as sources of political and economic empowerment and institutional innovation (voice). The capacity of actors and networks to influence and transform state institutions to advance the public interest in Quebec over the last twenty years is an illustration of empowerment and social change. Although under researched and under theorized, many such pragmatic approaches in the North and the South are constructing an alternative paradigm of local governance and development and are contributing to renewing democratic practice.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.289
Teacher spread0.271 · 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