Multi-label classification of evolving psychosocial concerns using prompt-based large language models
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
Classifying short, evolving textual descriptions of psychosocial concerns presents major challenges for traditional machine learning (ML) and deep learning (DL) models, which often struggle to adapt to emerging or overlapping categories. Multi-label classification further complicates the task, requiring flexible mechanisms capable of assigning multiple relevant labels to a single input. This study introduces a local, prompt-based classification approach that reframes the task as text generation, enabling dynamic and hierarchical labeling without retraining. The system leverages open-source large language models (LLaMA 3.1, LLaMA 3.3, Mistral, and Qwen) to associate each concern with one or more categories while generating contextual explanations to support professional interpretation. LLaMA 3.1 achieved the highest accuracy (97.2%) at the subcategory level, outperforming both classical baselines and other LLMs.
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 | 0.000 | 0.000 |
| 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