Lessons from Amilcar cabral for resistance research in leisure studies
Bibliographic record
Abstract
Few leisure scholars have defined resistance in published research. Those who have defined resistance adopted frameworks which centre norms, discourse, and ideologies rather than human rights or material needs. If the intent of resistance research is to make the world more just, a fuller accounting of the ways we might make a dent in the universe are needed. The present study fills this gap through a description Amilcar Cabral’s perspective on resistance. Cabral’s perspective is shared here because he successfully liberated Guinea and Cape Verde from Portuguese colonialism. We addressed this purpose through a thematic analysis of Cabral’s writings and scholarship about Cabral. In particular, we first share Cabral’s definition of resistance and examples of how he and the PAIGC put that definition into practice. We then explain the differences between Cabral’s perspective and those deployed in prior leisure research and the implications of those differences for future leisure research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".