Inferring the Number and Order of Embedded Topics Across Documents
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
Documents are often organized according to an embedded structure, where a set of documents covers a topic and gets extended to a more specialized topic. We refer to this structure as embedded topics and address the issue of inferring the number and order of topics in a given corpus. While this problem is akin to finding clusters of documents and has been addressed in numerous studies in areas such as topic modeling, information extraction and knowledge discovery, we show that existing approaches are not effective in the specific context of embedded topic structures, and propose a novel technique for that purpose. We also propose an approach to uncover the order of such embedded topics. To determine the number of topics, the proposed method relies on the analysis of eigenvalues of a conditional probability matrix derived from the document-term matrix. We use Kmeans to determine the actual topic clusters, and conditional probability computation to determine the order. We compare the performance of our method to alternative methods for determining clusters and dimensionality. Results show that the proposed approach can effectively derive the right number of topics and embedding structure order.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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