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
Null hypothesis significance tests are commonly used to provide a link between empirical evidence and theoretical interpretation. However, this strategy is prone to the "p-value fallacy" in which effects and interactions are classified as either "noise" or "real" based on whether the associated p value is greater or less than .05. This dichotomous classification can lead to dramatic misconstruals of the evidence provided by an experiment. For example, it is quite possible to have similar patterns of means that lead to entirely different patterns of significance, and one can easily find the same patterns of significance that are associated with completely different patterns of means. Describing data in terms of an inventory of significant and nonsignificant effects can thus completely misrepresent the results. An alternative analytical technique is to identify competing interpretations of the data and then use likelihood ratios to assess which interpretation provides the better account. Several different methods of calculating the likelihood ratios are illustrated. It is argued that this approach satisfies a principle of "graded evidence," according to which similar data should provide similar evidence.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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