Discourse analysis in international development studies: Mapping some contemporary contributions
Bibliographic record
Abstract
This paper critically examines work conducted by discourse analysts working in international development studies (IDS). During the 1990s, a number of authors introduced the study of speech, text and image as new paths toward understanding the causes of underdevelopment. This article highlights the authors who have worked on discourses on development and underdevelopment expressed by national and international governmental agencies and non-governmental organizations, scientific disciplines and specialized knowledge fields (including IDS). We focus in particular on the work of Chandra Mohanty, Arturo Escobar, James C. Scott, James Ferguson, Gilbert Rist and a selection of gender studies scholars. Beyond their differences, these discourse analysts in IDS share a rejection of mainstream analysis of underdevelopment. However, these authors remain marginalized in their own field of study and their work ought to be circulated in general discourse analysis circles.
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 | 0.024 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.023 | 0.029 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.026 | 0.020 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".