PathOS+: A New Realm in Expert Evaluation
Bibliographic record
Abstract
Expert evaluation is commonly employed in usability research as it is fast and cost-effective. However, as it heavily relies on evaluators’ expertise, it is associated with problems of subjective interpretation. This is particularly noticeable in the games user research (GUR) field as games often aim to reach broad audiences and complex experiences in comparison to other applications. One way to improve the problems associated with subjective interpretation is to augment the evaluation with data collected from players. However collecting data from players can be time-consuming, expensive, and challenging to construct representative player samples. Building on our previous research on automated agent-based playtesting, in this work-in-progress paper we detail our new project in supplementing expert evaluation with simulated player data generated by a configurable population of artificial intelligence (AI) players.
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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 | 0.051 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.013 |
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".