The Metric Used in the Global Health Impact Project: Implicit Values and Unanswered Questions
Bibliographic record
Abstract
Abstract The core aims of the Global Health Impact Project include incentivizing pharmaceutical companies for socially conscious production and promoting socially conscious consumption among consumers. Its backbone is a metric that computes the amount of illness burden alleviated by a pharmaceutical drug. This essay aims to assess the connection between values and numbers in the Global Health Impact Project. Specifically, I concentrate on two issues, the anonymity of illness burden and the distribution of health benefits. The former issue asks whether we should treat the illness burden of every person the same. The latter issue asks among whom health benefits should be fairly distributed. Examination of these issues begs for clarification of some of the key concepts of the Global Health Impact Project, such as the definition of essential medicines and the significance of national borders. Although this essay focuses on the two particular metric issues in the Global Health Impact Project, its core argument is applicable to other metrics for ethically motivated initiatives—to construct a metric for an ethically motivated initiative, it is not only important to articulate underlying concepts and values, but it is also important to operationalize them, so they are consistently reflected in the metric.
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 | 0.053 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.037 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".