Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Algorithmic genies built from growing computational capabilities bring risks like automating well-paying jobs, yet we suggest that if supplied with suitable goals and supporting infrastructure they can help in meeting many human needs. We argue that algorithmic genies can be harnessed to raise the baseline experience of people worldwide (raising the floor), especially if such harnessing is informed by wide consensus and deep evidence. Examples show how algorithmic genies could raise the floor for widely agreed human needs like health, education, and other components of the Social Progress Index. Ensuring that both the least well off and the majority share in the benefits of progress can help to ensure the floor is raised for all (floored progress). Floored progress can apply beyond basic human needs to problems that people across the economic spectrum struggle with (shared floors). We include three tables with illustrative opportunities, and conclude by summarizing the value of raising floors individually and in concert.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it