Bibliographic record
Abstract
When I first heard of meta-analysis in the mid-1980s,I was suspicious.After slaving for 3 years on a case-control study of renal adenocarcinoma that yielded just 3 papers, the idea of someone else getting a quick publication by quantitatively pooling my findings with other studies seemed parasitic, if not plagiarist. On the other hand, I was aware that reviews of scientific literature often simply bolstered the authors’ opinions by citing supportive studies and glossing over, dismissing or overlooking unsupportive studies. My suspicion was reduced by Meir Stampfer’s important paper pooling results of streptokinase trials. I decided meta-analysis might be appropriate for drug trials. However, I was troubled by the conflict between the apparent goal of metaanalysis –- to achieve statistical significance or tight confidence intervals — and Rothman’s and others’ criticism of p-values. Meta-analysis seemed to me to be a fancy name for combining results to get a better p-value.
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 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 | 0.127 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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; both teacher heads 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".