The Alleviation Mechanism of “The Predicament of Helping Others”: An Experimental Investigation
Bibliographic record
Abstract
In recent years, there are many news reports about “the predicament of helping the falling elderly”. Many scholars treat this predicament as a social and moral issue. Few scholars explore it from the holistic perspective. This thesis discusses the influencing factors of the predicament, including the positive and negative aspects of the news reports, individual sense of security and reward-punishment mechanism. Based on two priming experiments, this paper tests the following hypotheses: (1) the positive news report enhances people’s willingness to help the old; (2) the higher the level of the subject’ sense of security, the greater his willingness to raise the old up; (3) the reward-punishment mechanism also enhances people’s willingness to give a hand. These conclusions show that this predicament is not simply a moral or legal issue, but an outcome of the transaction among the parties involved, macro systems and micro contexts. In addition, this study also found that there are significant differences in people’s willingness to help the old between acquaintance society and strangers society. And, social justice has a positive impact on people’s tendency to help. Therefore, the news media should bear the social responsibility that guides positive public opinion when pursuing objective news report. At the same time, the state and society should design the appropriate reward-punishment mechanism to resolve the predicament. All of these should be based on methodological relationalism.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".