Epistemic injustice: women poppy cultivators in the opium production discourse of Afghanistan
Bibliographic record
Abstract
This article demonstrates how the prolonged absence and prejudicial treatment of women in discourse led to their loss of a conceptual framework and inability to articulate their experience to themselves and others. This study analyses the discourse of opium poppy cultivation in Afghanistan, focusing on international and national documentation of poppy cultivation between the Taliban’s fall in 2001 and their return to power in 2021. Applying a feminist epistemology and critical discourse analysis, this study conducts a textual analysis of the Annual Opium Survey Reports produced by the United Nations Office on Drugs and Crimes, the only formal institution presenting research-based reports on this subject. The study offers three interlinked findings. First, for 18 years, the epistemic erasure of women poppy cultivators from the national poppy cultivation narrative masculinised the poppy farming occupation and opium poppy-growing families. Second, for two years, the experiences of women opium poppy cultivators were prejudicially deflated, leading to the stereotypical genderisation of the farming occupation and opium-producing families. Third, these two factors – the epistemic erasure of women opium poppy cultivators and the deflation of their experiences – contributed to these women’s epistemic inability to self-identify as farmers, articulate their labour as farming, and claim economic contribution.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.034 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".