Amplifying invisibility: COVID‐19 and Zimbabwean migrant farm workers in South Africa
Bibliographic record
Abstract
Abstract How does the COVID‐19 pandemic impact migrant worker visibility? This paper examines how the pandemic underscores the invisibility of Zimbabwean migrant farm workers employed at ZZ2, one of the largest commercial farms in South Africa. I argue that Zimbabweans are made invisible in three ways. First, employer and state restrictions on mobility, alongside rising xenophobia in South Africa, leave migrant workers hyper‐visible to ZZ2 management, yet invisible to most people outside the farm. Second, ZZ2 avoids discussion of its migrant workforce in public forums, even as it faces increased scrutiny for its treatment of its workers during the pandemic. Third, the most prominent critic of ZZ2—the Economic Freedom Fighters (EFF)—grants migrant workers only a partial visibility as undifferentiated foreigners with no voice, a construction that ultimately maintains their invisibility at the company. Taken together, these interlocking forms of invisibilization diminish the structural and associational power of workers.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".