Do you want to publish a course? Click here

Belief in a Just or an unjust World

الاعتقاد بعالمٍ منصفٍ أو غير منصفٍ

1210   0   42   0 ( 0 )
 Publication date 2014
and research's language is العربية
 Created by Shamra Editor




Ask ChatGPT about the research

This research highlights the need to "Belief" from the perspective of psychology,scientific research related to confront threats and experiences of loss and overcome it proved the existence of two needs independent for interpretation (Davis, et. al., 1998, P561), first: the desire to realize something in a hostile disliked, that it a positive attitude; where expresses here often for existential re-evaluations (now I know what is important in my life), or for positive social experiences (I've realized that I can rely on my family); such new positive interpretations are more likely, whenever a person characterized by mental flexibility in thinking and tolerance with others (Dalbert, 1996, P36). Second: the desire to find the meaning of accidents, this has been discussed the need to find a deeper meaning research in the field of the just World. First, in this paper We'll show hypothesis of the Just World by Lerner (1965-1980) and will summarize the most important results of 40 years of research in the just world, and will dedicate the second part of the research for the organization of research just world in a separate form a comprehensive justice motive, so that distinguish between intuitive reactions and the reflectivity of the subject justice. In conclusion, clarify the relationship between religiosity and belief in the just world.

References used
.Agrawal, M. & Dalal, A. K. (1993). Beliefs about the world and recovery from myocardial infarction. The Journal of Social Psychology, 133, 385-394
.Allen, M. W., HungNg, S. & Leiser, D. (2005). Adult economic model and values survey: Cross-national differences in economic beliefs. Journal of Economic Psychology, 26, 159-185
Bègue, L. (2002). Beliefs in justice and faith in people: Just world, religiosity and interpersonal trust. Personality and Individual Differences, 32, 375-382
Bègue, L. & Muller, D. (2006). Belief in a just world as moderator of hostile attributional bias. British Journal of Social Psychology, 45, 117-126
Berscheid, E. & Walster, E. (1967). When does a harm-doer compensate a victim? Journal of Personality and Social Psychology, 6, 435-441
rate research

Read More

This research aims to trace the most important studies in the last two decades of the twentieth century. Those studies dealt with the cognitive effects of the family's just world experience to revel the effect of the personal justice and experience o n the adolescents' school success. The research sample covers 493 adolescent: 439 adolescent in the intact families and 54 adolescent with single mothers. Questionnaire that was used as a research tool, consisted of (49) item that are divided into three fields: (1) the Personal Belief in a just world (2), the experiences of mothers' justice (3) school success. The research main conclusion is that the personal belief in the just world and the mothers justice are both very important indicators in school success i.e. the adolescent aim to learning, school performance and grades). the more they belief that the world is fair with them personally, the more they tended to learn specially those in intact families compared with their peers in families with single mothers. In addition to, the more mother's treatment of children was fair, the more oriented their aims school towards learning and performance and promote the concept of school positive and avoid school failure, especially females were less likely to avoid school work, and more reflective the concept of school-positive than male and thus received school grades higher.
The ability to identify and resolve uncertainty is crucial for the robustness of a dialogue system. Indeed, this has been confirmed empirically on systems that utilise Bayesian approaches to dialogue belief tracking. However, such systems consider on ly confidence estimates and have difficulty scaling to more complex settings. Neural dialogue systems, on the other hand, rarely take uncertainties into account. They are therefore overconfident in their decisions and less robust. Moreover, the performance of the tracking task is often evaluated in isolation, without consideration of its effect on the downstream policy optimisation. We propose the use of different uncertainty measures in neural belief tracking. The effects of these measures on the downstream task of policy optimisation are evaluated by adding selected measures of uncertainty to the feature space of the policy and training policies through interaction with a user simulator. Both human and simulated user results show that incorporating these measures leads to improvements both of the performance and of the robustness of the downstream dialogue policy. This highlights the importance of developing neural dialogue belief trackers that take uncertainty into account.
The Shared Task on Hateful Memes is a challenge that aims at the detection of hateful content in memes by inviting the implementation of systems that understand memes, potentially by combining image and textual information. The challenge consists of three detection tasks: hate, protected category and attack type. The first is a binary classification task, while the other two are multi-label classification tasks. Our participation included a text-based BERT baseline (TxtBERT), the same but adding information from the image (ImgBERT), and neural retrieval approaches. We also experimented with retrieval augmented classification models. We found that an ensemble of TxtBERT and ImgBERT achieves the best performance in terms of ROC AUC score in two out of the three tasks on our development set.
Automatic metrics are commonly used as the exclusive tool for declaring the superiority of one machine translation system's quality over another. The community choice of automatic metric guides research directions and industrial developments by decid ing which models are deemed better. Evaluating metrics correlations with sets of human judgements has been limited by the size of these sets. In this paper, we corroborate how reliable metrics are in contrast to human judgements on -- to the best of our knowledge -- the largest collection of judgements reported in the literature. Arguably, pairwise rankings of two systems are the most common evaluation tasks in research or deployment scenarios. Taking human judgement as a gold standard, we investigate which metrics have the highest accuracy in predicting translation quality rankings for such system pairs. Furthermore, we evaluate the performance of various metrics across different language pairs and domains. Lastly, we show that the sole use of BLEU impeded the development of improved models leading to bad deployment decisions. We release the collection of 2.3M sentence-level human judgements for 4380 systems for further analysis and replication of our work.
Recent work has demonstrated that pre-training in-domain language models can boost performance when adapting to a new domain. However, the costs associated with pre-training raise an important question: given a fixed budget, what steps should an NLP practitioner take to maximize performance? In this paper, we study domain adaptation under budget constraints, and approach it as a customer choice problem between data annotation and pre-training. Specifically, we measure the annotation cost of three procedural text datasets and the pre-training cost of three in-domain language models. Then we evaluate the utility of different combinations of pre-training and data annotation under varying budget constraints to assess which combination strategy works best. We find that, for small budgets, spending all funds on annotation leads to the best performance; once the budget becomes large enough, a combination of data annotation and in-domain pre-training works more optimally. We therefore suggest that task-specific data annotation should be part of an economical strategy when adapting an NLP model to a new domain.
comments
Fetching comments Fetching comments
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا