Organising for voting rights of people with disabilities in Lebanon: reflections for activists
Bibliographic record
Abstract
Purpose Through a reflection on a recent voting rights campaign for people with disabilities in Lebanon, the article aims to contribute to a body of literature that seeks to challenge the marginalisation of people with disabilities; and aims to share our reflections with others interested in deeper thinking on the global impacts on their day‐to‐day work. Design/methodology/approach The article begins with a background discussion of the Lebanese context and the voting rights campaign, followed by an analysis of rights‐based work through a discussion of key issues faced by community activists. Findings The analysis highlights the impact of global forces on local grassroots work through the following issues: formalisation of relationships between national government organisations (NGOs); professionalisation within NGOs; contentious dealings with the state; the meanings and uses of diversity and international political relations. Originality/value There is a dearth of published studies on the disability‐rights movement in the Arab region; more specifically, the article provides a critical reflection on the changes facing the movement in Lebanon, where there are no published studies on the topic. This article would be of value to activists in other parts of the world interested in the changes facing activism on disability rights or other issues.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.040 | 0.019 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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".