Framing Manipulations in Contests: A Natural Field Experiment
Bibliographic record
Abstract
Exploiting findings that losses loom larger than gains, studies have shown that framing manipulations can increase productivity of workers. Using a natural field experiment that exogenously manipulates wage bonuses within contests in a Chinese high-tech manufacturing facility, we show that how loss aversion affects worker behavior critically depends on the incentive scheme as well as the framing manipulation. Four sets of two identical teams competed against each other to win a bonus given to the team, within a set, with the higher average hourly productivity over the week. In each set, the bonus was framed as a reward or gain for one team and as a punishment or loss for the other. Average weekly productivity was slightly higher under the loss treatment, but this increase was statistically insignificant. However, the team under the loss treatment was at least 35% more likely to win the contest. As teams' payoffs are based on relative productivity under a contest, framing effect is much stronger in terms of relative productivity. Finally, workers seemingly responded to the bonus by increasing the quality of production as well as quantity-defect rate fell as productivity increased.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".