GDE: General Data Exchange with Schema and Data Level Mappings.
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
Data exchange (DE) [5, 3] and data coordination [1, 2, 6] are two important settings that were introduced previously in the literature to resolve the problem of integrating information that resides in different sources. A DE setting moves data residing in independent applications, which refer to the same object using the same name, and accesses it through a new target schema. However, a data coordination setting allows the access of data residing in independent sources and that possibly belong to different sets of vocabularies, without necessarily exchanging it and while maintaining autonomy. Although a data coordination setting provides users with an amalgamated view of related information, this solution is not enough for applications that require a view of related information using a unified set of vocabularies for periodic reporting and decision making. We introduce a general data exchange (GDE) setting that extends DE settings to allow collaboration at the instance level, using a mapping table M , that specifies for each constant value in the source, the set of related (or corresponding) constant values in the target. We show in this paper that a GDE setting can be formalized using the knowledge exchange framework introduced in [4]. It allows us to store a target knowledge base (KB) which consists of a subset of the explicit data exchanged that is necessary to infer the full set of exchanged information using a set Σt of FO sentences. We identify in our work the class of “best” KBs to materialize and we define the set of certain answers.
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 | 0.002 | 0.003 |
| 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