Behind the mask: Random and selective masking in transformer models applied to specialized social science texts
Bibliographic record
Abstract
Transformer models such as BERT and RoBERTa are increasingly popular in the social sciences to generate data through supervised text classification. These models can be further trained through Masked Language Modeling (MLM) to increase performance in specialized applications. MLM uses a default masking rate of 15 percent, and few works have investigated how different masking rates may affect performance. Importantly, there are no systematic tests on whether selectively masking certain words improves classifier accuracy. In this article, we further train a set of models to classify fake news around the coronavirus pandemic using 15, 25, 40, 60 and 80 percent random and selective masking. We find that a masking rate of 40 percent, both random and selective, improves within-category performance but has little impact on overall performance. This finding has important implications for scholars looking to build BERT and RoBERTa classifiers, especially those where one specific category is more relevant to their research.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".