Location Reparameterization and Default Priors for Statistical Analysis
Bibliographic record
Abstract
This paper develops default priors for Bayesian analysis that reproduce familiar frequentist and Bayesian analyses for models that are exponential or location. For the vector parameter case there is an information adjustment that avoids the Bayesian marginaliza- tion paradoxes and properly targets the prior on the parameter of interest thus adjusting for any complicating nonlinearity; the details of this vector Bayesian issue will be investigated in detail elsewhere. As in wide generality a statistical model has an inference component structure that is approximately exponential or approximately loca- tion to third order, this provides general default prior procedures that can be described as reweighting likelihood in accord with a Jereys' prior based on observed information. Two asymptotic models, that have variable and parameter of the same dimension and agree at a data point to first derivative con- ditional on an approximate ancillary, produce the same p-values to third order for inferences concerning scalar interest parameters. With
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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 | 0.035 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".