From Discussion to Fist-fighting: Was Strategic Maneuvering Derailed during the Debate on the Presidential Age Limit Bill in Uganda?
Bibliographic record
Abstract
Of recent, due its impact on political events and socio-political processes like general elections and peace building, parliamentary discourse has become the object of research in Africa. In Uganda, in particular, at different times in the country’s history, it has been at the heart of fomenting conflict, but also key in fostering peace. It is of historic importance that we analyse how the controlled institutionalized parliamentary discourse during the presidential age limit debates degenerated to fist fighting and chair hurling in the Uganda Parliament. Using the pragma-dialectical, the rhetorical and linguistic approaches this study seeks to check the arguer’s commitment to pursuing a reasonable argumentation as s/he tries to discursively resolve the difference of opinion which is usually at the heart of parliamentary debates. Through a review of the atmosphere surrounding the presidential age limit debate and the two critical sessions of the relevant parliamentary discussions, the author tries to establish whether this discursive resolving of differences of opinion was achieved or whether there are factors that contributed to derailing the discursive strategic maneuvering.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".