The Geography of Development Studies: Leaving No One Behind
Bibliographic record
Abstract
Whereas the Millennium Development Goals sought reductions, the Sustainable Development Goals have set forth bold new objectives of leaving no one behind. This Commentary explores the continued geographic prioritization and exclusions within development studies research and some of the causes. The status quo is entrenching exclusion. A transformation of research, and the research community, is required to ensure that no one is left behind. Providing the evidence to support decision-making that is equitable and inclusive necessitates critical reflection of the exclusions that exist, along with innovation and creativity in how the research community can address gaps and support the more inclusive SDG agenda. Thought leadership and evidence will be the foundation that transforms our research and practice – if we, as a community of researchers, heed the call.
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 | 0.092 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.014 | 0.130 |
| Scholarly communication | 0.033 | 0.056 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.012 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 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".