Author Dirichlet Multinomial Allocation Model with Generalized Distribution (ADMAGD)
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
This paper introduces the Author Dirichlet Multinomial Allocation Model with Generalized Distribution (ADMAGD), a novel probabilistic topic model aimed at enhancing the representation of complex author-topic relationships. ADMAGD integrates a generalized Dirichlet distribution into the Author-Topic Model (ATM), enabling more nuanced modeling of authors topical preferences and word distributions within topics. We also propose a Gibbs sampling method for efficient parameter estimation. We evaluate ADMAGD on the 20-newsgroups and NIPS datasets, demonstrating its effectiveness over existing models such as LDA and ATM in scenarios involving intricate authorship patterns and topic-word relationships. The proposed model offers significant advantages for tasks such as authorship identification, content recommendation, and analysis of document creation patterns.
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 | 0.000 |
| 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