Nothing Normal About the New World: A Vision for Post-COVID International Development
Bibliographic record
Abstract
COVID-19 has exposed many fault lines in international development. As international staff were repatriated, the need to support communities with basics such as sanitation and hygiene once again fell to local organisations, who are often underfunded, in part because of the international development funding structures that are stacked against them. We argue that these structures lead to tokenistic partnerships, intervention design driven by short-term trends rather than the needs of communities, and ecological damage to the detriment of the very communities we claim to support. We argue that international development must take this opportunity to become more cognisant of and accountable for our carbon footprint, to develop new ways to support those organisations most closely linked to the communities they serve, to engage with the wider politics that has brought us to this point, and to commit to a future that redresses the inequalities of the past.
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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".