Chrome Extension
WeChat Mini Program
Use on ChatGLM

Against the Odds: Developing Underdog Versus Favorite Narratives to Offset Prior Experiences of Discrimination

One Health(2021)SCI 3区SCI 2区

Univ Penn | Univ Notre Dame | Johns Hopkins Univ

Cited 10|Views10
Abstract
Although considerable theory and research indicates that prior experiences of discrimination hinder individuals, it remains unclear what individuals can do to offset these repercussions in the context of their work and career. We introduce two distinct types of self-narratives-underdog and favorite-and test whether these types of personal stories shape the effects of prior experiences of discrimination on performance efficacy, which in turn impact performance. Across two time-lagged experiments with job seekers in both field and online settings, we theorize and find that underdog narratives are more effective than favorite narratives at moderating the effects of prior experiences of discrimination on performance through performance efficacy. Our results present new insights for theory and research on expectations, self-narratives, and resilience in the face of discrimination and adversity.
More
Translated text
Key words
Self-narratives,Discrimination,Expectations,Performance,Motivation,Self-efficacy,Job search,Adversity,Resilience
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
G Chen, SM Gully,D Eden
2001

被引用5624 | 浏览

Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest