Predicting classification errors using NLP-based machine learning algorithms and expert opinions
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
Various intentional and unintentional biases of humans manifest in classification tasks, such as those related to risk management. In this paper we demonstrate the role of ML algorithms when accomplishing these tasks and highlight the role of expert know-how when training the staff as well as, and very importantly, when training and fine-tuning ML algorithms. In the process of doing so and when facing well-known inefficiencies of the traditional F1 score, especially when working with unbalanced datasets, we suggest a modification of the score by incorporating human-experience-trained algorithms, which include both expert-trained algorithms (i.e., with the involvement of expert experiences in classification tasks) and staff-trained algorithms (i.e., with the involvement of experiences of those staff who have been trained by experts). Our findings reveal that the modified F1 score diverges from the traditional staff F1 score when the staff labels exhibit weak correlation with expert labels, which indicates insufficient staff training. Furthermore, the Long Short-Term Memory (LSTM) model outperforms other classifiers in terms of the modified F1 score when applied to the classification of textual narratives in consumer complaints. • Exploring classification performance evaluation for human-experience-related data. • Improving text classification performance by optimizing a word embedding process. • Using oversampling methods to improve the text classification under data imbalance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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