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
Checking the correspondence between two or more database instances and enforcing it is a procedure widely used in practice without however having been explored from a theoretical perspective. In this paper we formally introduce the data correspondence setting and its associated computational problems: checking the existence of solutions, and verifying whether a candidate is a solution, for three distinct types of solutions. Data correspondence is a generalization of data exchange and peer data exchange, and a special case of repairing inconsistent databases. We introduce a new class of dependencies, called semi-LAV, that properly includes both LAV and full dependencies, while retaining tractability for peer data exchange, data correspondence, and database repairs. We also introduce the concept of Σ-satisfying homomorphisms. This new type of homomorphism, together with recent advances, is essential in achieving tractability, while at the same time allowing a large class of dependencies, namely the aforementioned semi-LAV ones. We also show the intractability for a series of problems in the case of weakly acyclic tuple generating dependencies. This implies that many tractability results for weakly acyclic dependencies do not carry over to data correspondence; in these new settings we need to restrict the dependencies a bit further, yielding our semi-LAV dependencies.
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 | 0.000 | 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 | 0.001 |
| 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