An integrative theory of resource discrepancies
Bibliographic record
Abstract
Abstract A great deal of work in consumer psychology has been devoted to understanding how individuals manage resource discrepancies. This includes tangible resources – such as money, food, and products – as well as intangible resources – such as time, skills, and social relationships. Resource discrepancies can either be positive – as in the case of having substantial wealth – or negative – as in the case of poverty. Several constructs across the behavioral sciences have been introduced to describe how consumers perceive their various resource discrepancies including, but not limited to, power, social status, scarcity, inequality, and social class. However, little guidance is provided to understand when and why these resource‐based constructs can produce both overlapping and opposing consequences. This conceptual article provides a resolution to this issue by introducing an integrative theory that situates these constructs within the same unifying framework based on two fundamental dimensions: high (vs. low) personal control and self‐ (vs. other‐) dependence. Based on this framework, we offer eight testable propositions and develop a research agenda for academics interested in studying resource discrepancies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.005 |
| 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".