Implementasi Aspek Physio-Pleasure dalam Smart TV
Bibliographic record
Abstract
Pleasure define as the condition of consciousness or sensation induced by the enjoyment or anticipation of what is felt or viewed as good or desirable; enjoyment, delight, gratification. A usefull way of classifying different types of pleasure has been espoused by Canadian anthropologist Lionel Tiger. Tiger has made an extensive study of pleasure and has developed a framework for addressing pleasures issues. The framework models four conceptually distinct types of pleasure – physical, social, phsycological and ideological. Physio-pleasure is to do with the body and with pleasures derived from the sensing organs. They include pleasures connected with touch, taste and smell as well as feelings of sensual pleasures. Smart Television (Smart TV) is a combine of internet function and web 2.0 features. Physio-pleasure is the one of main factor on human interaction with smart television, especially on the virtual touchscreen navigation so that can make the real interaction. This paper discusses the implementation of physio-pleasure aspect in smart television, explanation abaout physio-pleasure, important of physio-pleasure and smart tv related to physio-pleasure.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 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".