Loss Aversion, Intellectual Inertia, and a Call for a More Contrarian Science: A Reply to Simonson & Kivetz and Higgins & Liberman
Bibliographic record
Abstract
Higgins and Liberman (2018) and Simonson and Kivetz (2018) offer scholarly and stimulating perspectives on loss aversion and the implications for the sociology of science of its acceptance as a virtual law of nature. In our view, Higgins and Liberman (2018) largely complement our conclusion that the empirical evidence does not support loss aversion. Moreover, in alignment with our call for a contextualized perspective, they provide an excellent discourse on how a more nuanced view of reference points and consumers’ regulatory focus enriches our understanding of the psychological impact of losses and gains. Simonson and Kivetz (2018) approached our perspective with skepticism, and, while they retain some skepticism, they express agreement on the larger point that loss aversion has been accepted too uncritically. Both commentaries point to a need for a critical reevaluation of prevailing paradigms. Here, we build on these perspectives, as well as our experience working on the topic of loss aversion, to call for structural changes to facilitate scholarly debate on science’s status quo.
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 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 | 0.001 | 0.001 |
| 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 | 0.000 |
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 teacher head, 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".