A Computer System for Automatic Evaluation of Researchers' Performance.
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
The increasing number of researchers and the limited financial resources has caused a tight competition among scientists to secure research funding. On the other side, it has become even harder for funding allocation organizations to evaluate the performance of researchers and select the best candidates. However, it seems that the current evaluation methods are highly correlated with subjective criteria. In addition, the subjective nature of peer-review as one the most common methods in scientific evaluation calls itself for an accurate complementary quantitative method to help the decision makers. This paper proposes an automatic computer system, which is based on machine learning techniques for predicting the performance of researchers. The proposed system uses various features of different types as the input to a complex machine learning module to predict the performance of a researcher in a given year. The method provides the decision makers with fair comparative results regardless of any subjective criteria. Our results show the high accuracy of the proposed system in predicting the performance of researchers.
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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 | 0.086 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.049 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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