Can Small Victories Help Win the War? Evidence from Consumer Debt Management
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 question of how people should structure goal-directed activity to maximize the likelihood of goal attainment is one of theoretical and practical significance. In particular, should people begin by attempting relatively easy tasks or more difficult ones? How might these differing strategies affect the likelihood of completing the overarching goal? The authors examine this question in the context of an important goal for a large number of consumers—getting out of debt. Using a data set obtained from a debt settlement firm, they find that (1) closing debt accounts is predictive of debt elimination regardless of the dollar balance of the closed accounts, whereas (2) the dollar balance of closed accounts is not predictive of debt elimination when controlling for the fraction of accounts closed. These findings suggest that completing discrete subtasks might motivate consumers to persist in pursuit of a goal. The authors discuss implications for goal pursuit generally and for consumer debt management specifically.
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 | 0.022 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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