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
Most prediction markets focus on events with a short time horizon such as forthcoming elections. Contracts are typically traded for periods measured in weeks, but rarely exceeding a year. There is great interest in using prediction markets for events with a long time horizon such as climate change outcomes. This paper develops an analytic framework for exploring the time horizon limitations of prediction markets and suggests a simple, practical solution: the market operator must invest cash holdings in a diversified financial portfolio that generates returns that reflect individual traders’ heterogeneous attitudes towards risk and return. The analytic framework identifies how the presence of an opportunity cost for investors reduces market liquidity through a participation constraint and biases the equilibrium price through an inherent money-at-risk asymmetry between long and short positions in a prediction market. This paper explores continuous outcome markets, which are relevant for science-related long-term predictions, along with familiar winner-takes-all markets.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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