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
Weighing complex sets of evidence (i.e., from multiple disciplines and often divergent in implications) is increasingly central to properly informed decision-making. Determining "where the weight of evidence lies" is essential both for making maximal use of available evidence and figuring out what to make of such evidence. Weighing evidence in this sense requires an approach that can handle a wide range of evidential sources (completeness), that can combine the evidence with rigor, and that can do so in a way other experts can assess and critique (transparency). But the democratic context in need of weight-of-evidence analysis also places additional constraints on the process, including communicability of the process to the general public, the need for an approach that can be used across a broad range of contexts (scope), and timeliness of process (practicality). I will compare qualitative and quantitative approaches with respect to both traditional epistemic criteria and criteria that arise from the democratic context, and argue that a qualitative explanatory approach can best meet the criteria and elucidate how to utilize the other approaches. This should not be surprising, as the approach I argue for is the one that most closely tracks general scientific reasoning.
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 | 0.003 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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