Matching Service Providers and Customers in Two-Sided Dynamic Markets
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents matching algorithms for two-sided dynamic service markets where service providers and customers form two disjoint sets and an agent from one side of the market can be matched only with an agent from the other side. We address the challenges derived from dynamic changes of the market. The algorithms are designed based on re-matching and repair-based matching models. The re-matching algorithm is straightforward and easy to implement. However, it does not have a mechanism to maintain matching consistency with the previous matching solution. Instead of computing a completely new matching solution, the repair-based matching algorithm maintain good matching consistency by repairing only the part of matching affected by the dynamic changes. In addition to better matching consistency, we show that the matching solutions generated by the repair-based matching algorithm are also stable.
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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 | 0.000 |
| 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 | 0.000 |
| 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