Augmenting Semantic Representation of Depressive Language: from Forums to Microblogs
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
Here we share our Adjusted Twitter word Embedding (ATE) and Depression Specific word Embedding (DSE) as described in our paper "Augmenting Semantic Representation of Depressive Language: from Forums to Microblogs", in the following formats respectively: ATE-Embedding.p is a pickle file which contains a Python dictionary of words and their corresponding vectors. DSE-Embedding.bin is a Gensim Word2Vec file and should be loaded using Gensim library (the files with .npy extensions are required to do this.) <strong>Example code for loading DSE-Embedding using Gensim:</strong> Word2Vec.load(embedding_path), tested on <em>Gensim-3.8</em> Please do cite our paper if you use our embedding or the idea of creating them.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.099 | 0.108 |
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