Κίνητρα φιλοκοινωνικής συμπεριφοράς: Αγνός αλτρουισμός ή ψυχολογικός εξαναγκασμός;
Bibliographic record
Abstract
Having as a starting point the dispute between the Empathy – Altruism Hypothesis and the Aversive Arousal Reduction Hypothesis, the present research focus on the study of the motives underlying prosocial behavior. Thus, an experimental process was designed in which male and female adults took part (N = 128). Participants first completed the Prosocial Tendencies Measure (PTM, Carlo & Randal, 2002) and the Toronto Empathy Questionnaire (TEQ, Spreng et al., 2009). Then, based on their answers, participants were assigned to eight groups and exposed to different experimental conditions (2 known – unknown x 2 in need of help – without need of help x 2 compel to receive help – absence of compel to receive help). Findings indicate that the level of empathy experiencing by the “benefactor” plays an important role in the performance of prosocial behavior. Nevertheless, the variable of familiarity with the person in need was found to be crucial in the performance of prosocial behavior as well as the need for help. Furthermore, findings suggest that the performance of prosocial behavior is a consequence of psychological pressure to the “benefactor” who, in turn, seems to use the performance of such a behavior as a means of psychological escape from a condition in which the request of help is salient.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".