Social Preference Parameters Impacting Financial Decisions Among Welfare Recipients
Bibliographic record
Abstract
This research study focuses on the social preference parameters and financial decisions among welfare populations receiving social benefits in Miami, Florida. Understanding the attitudes and primary motivations that shape financial decision-making is of great interest to economists, marketers, and other social scientists. The implications of developing a solid understanding of these attitudes and motivations are vast in terms of erecting tangible and sensitive workforce development policies to assist the specific population studied. This study is designed to determine whether significant differences exist in the strength of preference parameters between welfare participants and other populations. The preference parameters assessed in this paper were self-interest, altruism, trust, and reciprocity, both positive and negative. The control group in this study is college students. The results from the experiments show that welfare recipients exhibit similar behavioral patterns and make financial decisions in a manner similar to the general population. In other words, the control group and the experimental group did not differ significantly in their financial decision processes. This finding has several implications for how economists and policymakers assess and approach policymaking; nevertheless, the question remains whether or not there are other preference parameters that differ between the two groups.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".