Do I support that it’s good or oppose that it’s bad? The effect of support-oppose framing on attitude sharing.
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
The rise of social media has led to unprecedented opportunities for individuals to share, or express, their attitudes on social and political issues. What factors affect whether individuals choose to share? This research identifies a novel determinant of attitude sharing-support-oppose framing, defined as whether individuals think of their own attitude in terms of what they support or what they oppose. Support-oppose framing is distinct from attitude valence, as the same attitude can be framed in terms of support (e.g., I support that this policy is bad) or opposition (e.g., I oppose that this policy is good). Seven experiments, two correlational studies, and one field study provide evidence for a support-oppose framing effect, whereby individuals are more likely to share attitudes framed in terms of positions they support rather than positions they oppose. This effect occurs via two pathways. In the first, support-framed attitudes are viewed as more value expressive, which facilitates greater attitude sharing. In the second, support-framed attitudes are believed to promote more positive impressions, which also leads to greater sharing. This effect is attenuated when individuals' typical impression-management goals are relaxed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it