Bibliographic record
Abstract
Though computational models take a lot of effort to build, a model is generally not useful unless it can help people to understand the world being modelled, or the problem the model is intended to solve. A useful model allows people to make useful predictions about how the world will behave now and possibly tomorrow. Validation is the last step required in developing a useful Bayesian model. The goal of validation is to gain confidence in a model and to demonstrate and prove that a model produces reliable results that are closely related to the problems or issues in which the model is intended to address. The goal of the Chapter is to provide the reader with a basic understanding of the validation process and to share with them key lessons learned from the model of social capital presented in the book. While sensitivity analysis is intended to ensure that a Bayesian model is theoretically consistent with goals and assumptions of the modeller (how the modeller views the world) or the accuracy of sources of data used for building the model, the goal of validation is to demonstrate the practical application of the model in real world settings. This Chapter presents the main steps involved in the process of validating a Bayesian model. It illustrates this process by using examples drawn from the Bayesian model of social capital.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".