Bibliographic record
Abstract
A p-value is a number between 0 and 1 that is extremely useful in interpreting research results. Using comparison of the means of two samples as an example, a p-value <0.05 suggests that there is enough evidence to presume a real difference between groups from which the samples were drawn (that the "null hypothesis" can be rejected). We say that the difference between the means is statistically significant. However, it isn't iron clad proof and there is still a chance that there is really no difference. Furthermore, a statistically significant difference may not be clinically significant if it is not enough to appreciably affect patient outcomes. We describe the theory behind p-values and some common errors in interpretation.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.288 | 0.850 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.016 | 0.028 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".