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
"Evaluation as a service" (EaaS) refers to a family of related evaluation methodologies that enables community-wide evaluations and the construction of test collections on documents that cannot be easily distributed. In the API-based approach, the basic idea is that evaluation organizers provide a service API through which the evaluation task can be completed, without providing access to the raw collection. One concern with this evaluation approach is that the API introduces biases and limits the diversity of techniques that can be brought to bear on the problem. In this paper, we tackle the question of API bias using the concept of retrievability. The raw data for our analyses come from a naturally-occurring experiment where we observed the same groups completing the same task with the API and also with access to the raw collection. We find that the retrievability bias of runs generated in both cases are comparable. Moreover, the fraction of relevant tweets retrieved through the API by the participating groups is at least as high as when they had access to the raw collection.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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