Bibliographic record
Abstract
Apparel is the primary material that a person consulted while creating and identity which is not only who actually he is, but also who he wants to be.In his social life or working life, people dress for impressing others because of mmany effective reasons, or pretending someone else or just to get what he wants.Thus he tells something about himself and over these images the society relates the look with his life-style.Basically, fashion refreshes itself for this deceptions.Human being is able to cover himself in any surroundings even he is most visible in.So, he is influenced by what, in a virtual world where is the most available atmosphere to hide "self"?While he is creating his digital identity called avatar, does he follow his own example or draws a sample of the simulation of a person he wants to be?In this case, it's studied that; what criterions the gamers take in their costume and image selections during the creation of their visual characters and also this consciousness or underconsciousness is being understood by other gamers in the digital games like The Sims, The Sims Social, Second Life by questing the gamers in several ages, occupations and genders.Finally, it's been highlighted the importance of apparel in the process of creating a digital ID.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.016 |
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".