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
While machine learning is a powerful tool for the analysis and classification of complex real-world datasets, it is still challenging, particularly for developers with limited expertise, to incorporate this technology into their software systems. The first step in machine learning, data labeling, is traditionally thought of as a tedious, unavoidable task in building a machine learning classifier. However, in this paper, we argue that it can also serve as the first opportunity for developers to gain insight into their dataset. Through a Label-and-Learn interface, we explore visualization strategies that leverage the data labeling task to enhance developers' knowledge about their dataset, including the likely success of the classifier and the rationale behind the classifier's decisions. At the same time, we show that the visualizations also improve users' labeling experience by showing them the impact they have made on classifier performance. We assess the visualizations in Label-and-Learn and experimentally demonstrate their value to software developers who seek to assess the utility of machine learning during the data labeling process.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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