Player Typology in Theory and Practice
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
Player satisfaction modeling depends in part upon quantitative or qualitative typologies of playing preferences, although such approaches require scrutiny. Examination of psychometric typologies reveal that type theories have—except in rare cases—proven inadequate and have made way for alternative trait theories. This suggests any future player typology that will be sufficiently robust will need foundations in the form of a trait theory of playing preferences. This paper tracks the development of a sequence of player typologies developing from psychometric type theory roots towards an independently validated trait theory of play, albeit one yet to be fully developed. Statistical analysis of the results of one survey in this lineage is presented, along with a discussion of theoretical and practical ways in which the surveys and their implied typological instruments have evolved.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 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 it