Bibliographic record
Abstract
The current study examined the effects of volume and valence of user ratings in a movie rating Web site onindividuals’ perception about the user ratings and also on individuals’ willingness to express their opinionsonline. In study 1, undergraduate participants were randomly assigned to one of 2 (volume: low and high) × 2(valence: positive and negative) conditions. In study 2, undergraduate participants were randomly assigned toone of 4 (volume: low, high, super high, and mega high) × 2 (valence: positive and negative) conditions. Thefindings showed that individuals perceived others to be more affected by user ratings than themselves, that theperceived effect of user ratings on others was positively related to individuals’ willingness to express opinions,and that the extent to which individuals’ own rating differed from the valence of user ratings was positivelyrelated to willingness to express opinions in the negative valence condition, but negatively related to willingnessto express opinions in the positive valence condition. This study applied social science theories to betterunderstand the mechanism of individuals' opinion expression online. By manipulating user ratings aboutunreleased movies, this study controlled potential effects of participants' familiarity with rated movies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.014 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".