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Democracy re-examined: Ghanaian social movement learning and the re-articulation of learning in struggle

2011· article· en· W217422365 on OpenAlexaff
Jonathan Langdon

Bibliographic record

VenueStudies in the Education of Adults · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsSocial movementDemocracySociologyGlobalizationContext (archaeology)Social learningPolitical sciencePolitical economyPoliticsPedagogyLaw

Abstract

fetched live from OpenAlex

Ghana has been identified as an important example of democracy in Africa, yet the story of this democratic success overlooks the crucial role social movement activism and learning have played in locally reconfiguring and deepening what democracy means. A key dimension of this overlooked-story is ongoing efforts to contest both local and global power relations. This article presents results from a participatory assessment by Ghanaian activist-educators embedded in these movements of social movement learning in this African democratic context, and adds to contemporary efforts to re-examine how movement learning contributes to challenging globalisation through deepened democracy. Foley's (1999) notion of learning in struggle represents a key lens through which this collaborative understanding emerged. However, this notion is re-articulated in the study in three ways to capture 1) long-term evolving incidental learning, 2) intensive event-based incidental learning, and 3) emergent normative learning approaches. This final distinction led to a rich debate as to which emergent normative approach would be most effective in learning to struggle against globalisation. For many in the study, the ongoing learning processes of livelihood and resource defence movements hold the most promise, especially in light of recent oil discoveries in Ghana that are likely to heighten the intensity of this globalisation.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

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

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 head0.044
GPT teacher head0.357
Teacher spread0.313 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations14
Published2011
Admission routes1
Has abstractyes

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