Practical application of meta-analysis results: avoiding the double use of data
Bibliographic record
Abstract
Meta-analysis is an important new tool for synthesizing scientific knowledge from many previous studies. In fisheries, meta-analyses can be used to obtain prior distributions or penalty functions for parameters used in stock assessment models. Two types of results are generally published in a meta-analysis: Type A, the updated results for each stock used in the meta-analysis, and Type B, the results that would best describe a new stock. Including these results in assessments for the individual stocks would result in double use of the data if the assessments include the input data used in the meta-analyses, which they typically would. To solve this problem, we recommend that an additional form of results should be reported in meta-analyses: Type C, the results for a new stock obtained by sequentially excluding each stock's data set and repeating the meta-analysis. Type C results should be used whenever the assessment input data overlap with the meta-analysis input data, avoiding the double use of data. We illustrate the impact of this reporting change on the results of a recent meta-analysis.
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 | 0.511 | 0.802 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.016 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".