Sticky Frames and What's in a Name: Frames Stick to Particular Objects
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
A growing literature on sequential framing effects has documented a negativity bias: In many contexts, attitudes change less when framing switches from negative-to-positive (vs. positive-to-negative). However, it is unclear whether this negativity bias sticks to one attitude object or generalizes beyond it. Novel paradigms in two experiments yielded strong evidence for the first possibility and tentative evidence for the second: Switching to a different object (vs. same object) across time points reduced the negativity bias in reframing. In contrast, superficially rebranding an object (just changing its name) did not reduce negativity bias. The experiments also provide the first evidence that positive frames are somewhat sticky: A positive initial frame somewhat attenuated the impact of a negative subsequent frame on attitudes. The findings are consistent with the possibility that once an object is framed negatively or positively, that conceptualization sticks in the mind and resists subsequent reframing—especially for negative frames.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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