New Issues, New Perspectives: Implications for International Development Studies
Bibliographic record
Abstract
This paper examines the introduction of “new” contextual issues and analytical concepts within International Development Studies (ids) that have shaped the current penchant for the “development and other” focus. Contextual issues (e.g., poverty, aid, health, education, hiv/aids, new information and communication technologies [icts], peacekeeping, and conflict resolution) and highly contested analytical concepts (e.g., gender, environment or sustainability, civil society, globalization, good governance, social capital, capacity building, participation, empowerment, and security) have been wedded to core development questions. This paper examines both how this “development and…” focus has shaped development studies and provided the foundations for a new and invigorated generation of development theories. It analyzes how the introduction of “new” contextual issues and analytical concepts has challenged, or failed to challenge, our conceptual understanding of development issues. It provides a chronology of how and when such issues and concepts are introduced, and how and why they are taken on and off the development policy agenda set by multilateral and bilateral agencies.
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 | 0.029 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.009 | 0.047 |
| Scholarly communication | 0.027 | 0.039 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 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".