Monitoring semantic relatedness and revealing fairness and biases through trend tests
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
An emerging application domain concerning content-based recommender systems provides a better consideration of the semantics behind textual descriptions. Traditional approaches often miss relevant information due to their sole focus on syntax. However, the Semantic Web community has enriched resources with cultural and linguistic background knowledge, offering new standards for word categorization. This paper proposes a framework that combines the information extractor ReVerb with the WordNet taxonomy to monitor global semantic relatedness scores. Additionally, an experimental validation confronts human-based semantic relatedness scores with theoretical ones, employing Mann–Kendall trend tests to reveal fairness and biases. Overall, our framework introduces a novel approach to semantic relatedness monitoring by providing valuable insights into fairness and biases. • We combined ReVerb and WordNet to perform global semantic relatedness scores. • Some historical similarity measures were redefined using a logical formalism. • Our framework monitors the relatedness scores through a segmental views-based interface. • We confronted humans-based semantic similarity scores with the theoretical ones. • We performed a set of Mann–Kendall trend tests in order to reveal fairness and biases.
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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 | 0.000 | 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 | 0.001 | 0.009 |
| Open science | 0.001 | 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