Why this work is in the frame
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Bibliographic record
Abstract
This article gives a brief overview of near sets. The proposed approach in introducing near sets is to consider a set theory-based form of nearness (proximity) called discrete proximity. There are two basic types of near sets, namely, spatially near sets and descriptively near sets. By endowing a nonempty set with some form of a nearness (proximity) relation, we obtain a structured set called a proximity spaces. Let $${\mathcal{P}(X)}$$ denote the set of all subsets of a nonempty set X. One of the oldest forms of nearness relations p (later denoted by δ) was introduced by E. Čech during the mid-1930s, which leads to the discovery of spatially near sets, i.e., those sets that have elements in common. That is, given a proximity space (X, δ), for any subset $${A \in \mathcal{P}(X)}$$ , one can discover nonempty nearness collections $${\xi(A) = \{B \in \mathcal{P}(X): A \, \delta \, B\} }$$ . Recently, descriptively near sets were introduced as a means of solving classification and pattern recognition problems arising from disjoint sets (i.e, sets with empty spatial intersections) that resemble each other. One discovers descriptively near sets by choosing a set of probe functions Φ that represent features of points in a set and endowing the set of points with a descriptive proximity relation δ Φ and obtaining a descriptively structured set (called descriptive proximity space). Given a descriptive proximity spaces (X, δ Φ), one can discover collections of subsets that resemble each other. This leads to the discovery of descriptive nearness collections $${\xi_{\Phi}(A) = \{B \in \mathcal{P}(X): A \,\delta_{\Phi} \, B\} }$$ . That is, if $${B \in \xi_{\Phi}(A)}$$ , then A δ Φ B (relative to the chosen features of points in X, A resembles B). The focus of this tutorial is on descriptively near sets.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| 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