Democracy re-examined: Ghanaian social movement learning and the re-articulation of learning in struggle
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".