When “data” are not data: the pitfalls of post hoc analyses that use stock assessment model output
Bibliographic record
Abstract
The practice of treating stock assessment model output as data in subsequent modeling efforts is becoming more common, aided in part by the growing availability of online repositories of assessment results (misleadingly referred to as “data” bases). Such modeling exercises frequently overlook the uncertainty in the assessment output, the potential bias in estimates and correlation between estimates, and the structural assumptions of the original assessment model. We provide examples of post hoc analyses and discuss the problems in each case. We suggest alternative approaches that could have avoided using assessment model output altogether or suggest analyses that may have exposed the pitfalls of such methods. Whenever possible, we suggest not using stock assessment model output as data in post hoc analyses. If using assessment model output as data is unavoidable, then to address some aspects of the uncertainties associated with using assessment model estimates, we suggest collaborating with lead assessment scientists, sensitivity analyses, errors-in-variables methods, and cross-validation methods. Such additional work is imperative if research that uses stock assessment output as data is to make robust and meaningful contributions to stock assessment methodology and management decisions.
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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 | 0.281 | 0.612 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".