Enhancing Reliability through Screening and Segmentation: An Online Video Subjective Quality of Experience Case Study
Bibliographic record
Abstract
In this paper we examine the reliability of subjective rating judgments along a single dimension, focusing on estimates of technical quality produced by integrity impairments and failures (non-accessibility, and non-retainability) associated with viewing video. There is often considerable variability, both within and between individuals, in subjective rating tasks. In the research reported here we consider different approaches to screening out unreliable participants. We review available alternatives, including a method developed by the ITU, a method based on screening outliers, a method based on strength of correlations with an assumed “natural” ordering of impairments, and a clustering technique that makes no assumptions about the data. We report on an experiment that assesses subjective quality of experience associated with impairments and failures of online video. We then assess the reliability of the results using a correlation method and a clustering method, both of which give similar results. Since the clustering method utilized here makes fewer assumptions about the data, it may be a useful supplement to existing techniques for assessing reliability of participants when making subjective evaluations of the technical quality of videos.
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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 | 0.022 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".