When “data” are not data: the pitfalls of post hoc analyses that use stock assessment model output
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
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.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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