On the promise and perils of citizenship: heuristic concepts, Zimbabwean example
Bibliographic record
Abstract
Increasingly, struggles in the name of citizenship inspire and catch the imagination and support of individuals and groups found in a variety of locales within a nation as well as transnational spaces. At the same time, their consequences may be quite different from the assumptions and dreams of those involved in perpetuating and imagining these struggles. To analyse how new social citizenship claims can embolden and channel struggles in particular directions with varied results – the promise and perils of citizenship more broadly – I suggest that one should pay attention to the promulgators of such visions of citizenship, the techniques of promoting their claims and the cultural politics and political economies of belonging in the locales of mobilization. Drawing on an ethnographic example of a farm labour struggle in the late 1990s in Zimbabwe, I explore the importance of attending to wider shifts in the political importance of citizenship as well as its entanglement in particular localities. Through examining how farm workers are situated through such struggles, I show the promise and limits of citizenship in addressing social justice concerns of a group historically marginalized through racialized, classed and gendered processes.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.012 | 0.060 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".