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
Association rules in data mining are implications between attributes of objects that \nhold in all instances of the given data. These rules are very useful to determine the properties of the data such as essential features of products that determine the purchase \ndecisions of customers. Normally the data is given as binary (or crisp) tables relating \nobjects with their attributes by yes-no entries. We propose a relational theory for \ngenerating attribute implications from many-valued contexts, i.e, where the relationship between objects and attributes is given by a range of degrees from no to yes. This \ndegree is usually taken from a suitable lattice where the smallest element corresponds \nto the classical no and the greatest element corresponds to the classical yes. Previous \nrelated work handled many-valued contexts by transforming the context by scaling \nor by choosing a minimal degree of membership to a crisp (yes-no) context. Then the \nstandard methods of formal concept analysis were applied to this crisp context. In \nour proposal, we will handle a many-valued context as is, i.e., without transforming \nit into a crisp one. The advantage of this approach is that we work with the original \ndata without performing a transformation step which modifies the data in advance.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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