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
We consider a set of users who are located along a pipeline with a single source. These users consume a good that is extracted from the source and flows downstream, with diminishing marginal returns for each user. In addition, flows along each edge in the pipeline create negative externalities, which are nondecreasing as a function of flow. The users cooperate toward obtaining group welfare maximization. In both the continuous and discrete cases, we obtain the group optimal solutions, and we then use cooperative game theory to determine how best to allocate the damages, using optimistic and pessimistic formulations for the characteristic function. Using core stability as our guiding principle, we provide a set of stable allocations that apportions the damages at a location among the set of downstream users, notably an average damage allocation and a marginal damage allocation. Given that the joint optimization forces agents to reduce (unequally) their consumption, we also examine the Shapley value of the optimistic game, also in the core, that allows to compensate agents who have sacrificed their consumption for the benefit of the group. Finally, we show that our pipeline externalities model generalizes some well-known problems from the literature, including the river sharing problem of Ambec and Sprumont 2002 and the joint production problem of Moulin and Shenker 1992.
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 | 0.005 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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