“You can’t see what you’ve never had to live”—Cultivating imagination and solution spaces in global health and development
Bibliographic record
Abstract
Elon Musk, the world's wealthiest man, recently declared on X (Twitter) that "The path to solving hunger, disease and poverty is AI and robotics" [1].He did not mention taxing the rich as a potential solution.Musk's power and privilege limit or shrink his imagination, for he cannot see what he has never had to live.Musk's statement reflects a deeper systemic truth-that the most visible "solutions" to hunger, poverty, and disease are often imagined by those who have been structurally insulated from scarcity, dispossession or violence.Because they never have to face the consequences of their own band-aid solutions, their imaginations are not shaped by real stakes in the matter.Furthermore, they can worsen our shared realities by monopolising which futures are considered "realistic" or worthy.It is no surprise that Musk led the efforts to defund United States (U.S.) government aid and science organizations, and worsened hunger and disease [2].
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.061 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".