CNG Text Classification for Authorship Profiling Task Notebook for PAN at CLEF 2013.
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
We describe our participation in the Author Profiling task of the PAN 2013 competition. The task objective is to determine the age and the gender of an author of a document. We applied the Common N-Gram (CNG) classifier (Keselj et al., 2003) to this task. The CNG classifier uses a dissimilarity measure based on the differences in the frequencies of the character n-grams that are most common in the considered documents. To train the classifier, a class is represented by one class document created by concatenating the training documents belonging to the class. A sample document is labelled by the class with the minimum dissimilarity. For the six class classification (combinations of two possible gender labels and three possible age labels) we achieved the accuracy of 0.2814 on the English test dataset and 0.2592 on the Spanish test dataset. Our results are below the medians of the results of the competition participants.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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