Applying the methodology of construction of weights and classification of texts from the article "Weighting construction by bag-of-words with similarity-learning and supervised training for classification models in Court text documents" in a set of data available on the internet
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
Traditional models of bag-of-words for text classification are unable to identify weights for the co-occurrence of terms, and, mainly, for this reason, they are being replaced by models of word embedding. This capsule contains the implementation of the methodology proposed in the article "Weighting construction by bag-of-words with similarity-learning and supervised training for classification models in Court text documents". Two computational representations of the datasets are used: binary and frequency, for supervised training of nine classification technologies: random forest, multilayer perceptron neural networks, adaptive boosting, gradient boosting, Gaussian process, support vector machine, Naive Bayes, k-nn and decision trees. The results are compared and assessed using the accuracy, f-measure, precision, and recall metrics.
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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 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