The Effect of Partitions on Controlling Consumption
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 authors demonstrate that partitioning an aggregate quantity of a resource (e.g., food, money) into smaller units reduces the consumed quantity or the rate of consumption of that resource. Partitions draw attention to the consumption decision by introducing a small transaction cost; that is, they provide more decision-making opportunities so that prudent consumers can control consumption. Thus, people are better able to constrain consumption when resources associated with a desirable activity (which they are trying to control) are partitioned rather than when they are aggregated. This effect of partitioning is demonstrated for the consumption of chocolates (Study 1) and gambles (Study 2). In Study 3, process measures reveal that partitioning increases recall accuracy and decision times. Importantly, the effect of partitioning diminishes when consumers are not trying to regulate consumption (Studies 1 and 3). Finally, Study 4 explores how habituation may decrease the amount of attention that partitions draw to consumption. In this context, partitions control consumption to a greater extent when the nature of partitions changes frequently.
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 | 0.110 | 0.074 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it