Neoliberal co-optation of feminist discourse within agricultural transformation policy and practice across sub-Saharan Africa
Bibliographic record
Abstract
Motivation: The apparent ‘success’ of centring gender equality in international development agendas calls into question whose version of gender it is that is mainstreamed and what values and knowledge are upheld within this. Drawing on critiques of the aid-driven development landscape, this thesis explores how the gender ‘buzzwords’ which have long animated the field of gender and development have now been absorbed within agricultural transformation discourse. \nPurpose: Through analysis of key gender-development buzzwords and policy paradigms: gender mainstreaming, women’s empowerment, and smart economics, the overarching aim is to explore how gender inequality is discursively framed as a policy ‘problem’ within agricultural transformation discourse, and how this then shapes how it is approached within policy and practice across sub-Saharan Africa. \nApproach and Methods: A qualitative approach to data collection and analysis - combining discourse analysis of key policy and practitioner documents and key-informant and expert interviews – is utilised to explore how the discursive framing of gender inequality co-opts feminist discourse, and hence shapes how gender relations are understood and approached within development. \nFindings: Through linking gender equality and empowerment with agricultural productivity and profitability, gender inequality - and specifically women’s disempowerment - is discursively framed as a barrier to agricultural productivity and transformation. These donor-driven gender narratives impose a reductive and simplistic version of gender couched in mainstream Western ideals of what ‘empowerment’ entails, and promotes the continued victimisation and nstrumentalisation of rural African women. \nContribution and Policy Implications: Findings demonstrate that these gender buzzwords and myths have been purposefully absorbed into agricultural transformation discourse where they are reinforced by powerful hegemonic donors through control of narratives, funding and reporting relationships in development projects, programmes and policy. An important contribution is through promoting the potential that decolonial and African feminist literature offers in \nconstructing counter-hegemonic discourses that disrupt neoliberal framings of the Third World Woman that underlie these myths and buzzwords.
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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 | 0.030 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.017 | 0.063 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".