Bibliographic record
Abstract
# Contents This replication package contains the inputs, outputs, and generated artifacts of the rapid prototyping workflow in EpiMDE project. ## 📂 input_models/ This folder contains the three original input models provided by Carol. All models are represented in a format that conforms to the **EpiMDE metamodel**. ## 📄 fca_output.pdf This file contains the output produced by **FCA4j** during the **Feature Identification** step of the rapid prototyping process. It represents the concept lattice used to extract feature clusters. ## 📄 features__aka_clusters_with_labels.csv This file contains the **features of Carol’s models**. These features correspond to the **clusters extracted from the FCA lattice** (shown in `fca_output.pdf`), **after labels were assigned by Carol**. ## 📄 logical_dependencies.txt This file contains the **identified logical dependencies between features**, produced during the **Feature Relationship Identification** step of the approach. ## 📂 output_prototype_model/ This folder contains the **final output of the rapid prototyping workflow**. It includes the generated **prototype model**, which is composed of the **selected features from the input models** and is represented in a format that conforms to the **EpiMDE metamodel**.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.173 | 0.065 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".