Applying behavioral economic theory to problematic Internet use: An initial investigation.
Bibliographic record
Abstract
The widespread availability of the Internet has had profound social, educational, and economic benefits. Yet, for some, Internet use can become compulsive and problematic. The current study seeks to apply a behavioral economic framework to Internet use, testing the hypothesis that, similar to other addictive behaviors, problematic Internet use is a reinforcer pathology, reflecting an overvaluation of an immediately acquirable reward relative to prosocial and delayed rewards. Data were collected through Amazon's Mechanical Turk data collection platform. A total of 256 adults (Mage = 27.87, SD = 4.79; 58.2% White, 23% Asian; 65.2% had an associate degree or greater) completed the survey. Measures of delay discounting, consideration of future consequences, Internet demand, and alternative reinforcement all contributed unique variance in predicting both problematic Internet use and Internet craving. In aggregate models controlling for all significant predictors, alternative reinforcement and future valuation variables contributed unique variance. Individuals with elevated demand and discounting were at greatest risk for problematic Internet use. Consistent with behavioral economic research among substance abusing samples, individuals engaging in heavy Internet use report elevated motivation for the target behavior coupled with diminished motivation for other potentially rewarding activities, especially those associated with delayed reward. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".