“I Identify with Her,” “I Identify with Him”: Unpacking the Dynamics of Personal Identification in Organizations

Despite recognizing the importance of personal identification in organizations, researchers have rarely explored its dynamics. We define personal identification as perceived oneness with another individual, where one defines oneself in terms of the other. While many scholars have found that personal identification is associated with helpful effects, others have found it harmful. To resolve this contradiction, we distinguish between three paths to personal identification—threat-focused, opportunity-focused, and closeness-focused paths—and articulate a model that includes each. We examine the contextual features, how individuals’ identities are constructed, and the likely outcomes that follow in the three paths. We conclude with a discussion of how the threat-, opportunity-, and closeness-focused personal identification processes potentially blend, as well as implications for future research and practice

Deep Learning Face Representation by Joint Identification-Verification

The key challenge of face recognition is to develop effective feature representations for reducing intra-personal variations while enlarging inter-personal differences. In this paper, we show that it can be well solved with deep learning and using both face identification and verification signals as supervision. The Deep IDentification-verification features (DeepID2) are learned with carefully designed deep convolutional networks. The face identification task increases the inter-personal variations by drawing DeepID2 extracted from different identities apart, while the face verification task reduces the intra-personal variations by pulling DeepID2 extracted from the same identity together, both of which are essential to face recognition. The learned DeepID2 features can be well generalized to new identities unseen in the training data. On the challenging LFW dataset, 99.15% face verification accuracy is achieved. Compared with the best deep learning result on LFW, the error rate has been significantly reduced by 67%

Mining online diaries for blogger identification

In this paper, we present an investigation of authorship identification on personal blogs or diaries, which are different from other types of text such as essays, emails, or articles based on the text properties. The investigation utilizes couple of intuitive feature sets and studies various parameters that affect the identification performance. Many studies manipulated the problem of authorship identification in manually collected corpora, but only few utilized real data from existing blogs. The complexity of the language model in personal blogs is motivating to identify the correspondent author. The main contribution of this work is at least three folds. Firstly, we utilize the LIWC and MRC feature sets together, which have been developed with Psychology background, for the first time for authorship identification on personal blogs. Secondly, we analyze the effect of various parameters, and feature sets, on the identification performance. This includes the number of authors in the data corpus, the post size or the word count, and the number of posts for each author. Finally, we study applying authorship identification over a limited set of users that have a common personality attributes. This analysis is motivated by the lack of standard or solid recommendations in literature for such task, especially in the domain of personal blogs. The results and evaluation show that the utilized features are compact while their performance is highly comparable with other larger feature sets. The analysis also confirmed the most effective parameters, their ranges in the data corpus, and the usefulness of the common users classifier in improving the performance, for the author identification task

Supplier search in industrial clusters: Sheffield metal working in the 1990s

Industrial clusters can be characterised by high levels of personal interaction between the owners/managers of firms. It has been argued that within industrial clusters community and firm tend to merge. One result of the notion of a cluster as a community is that it might be expected that personal interaction between members of the community will have an important influence on intra-cluster trading patterns. Whereas there appears to be a number of anecdotal stories of the impact of the 'personal' upon the 'economic' within clusters we do not know whether such relationships are uniquely part of cluster behaviour or whether they are found more widely within the industrial system. The paper reports the result of a new interview survey of seventy small metal working firms in the Sheffield metal working cluster in the UK. Although dealing with a traditional industrial sector the analysis is focused upon contemporary business patterns. It explores the ways in which owner/managers of small metal working firms search for new suppliers. In the empirical analysis the search process is conceptualised as being characterised by two stages: the identification of potential new suppliers and the selection of a specific new supplier. The research is in undertaken in two parts. It first measures the role of the personal networks of the owner/managers of small firms in the identification and selection of suppliers. Second, the research examines whether personal factors are more important in the identification and selection of within cluster suppliers than in the identification and selection of suppliers based outside the cluster. It is shown that, overall, personal networks are of major significance in the identification of suppliers and that information received from third parties are more important than direct contacts between the owner/manager and the potential supplier. However, in the selection decision, price and availability are dominant considerations and personal factors such as trust and reputation of only minor significance. It was not possible to identify a cluster effect in disaggregation of the data to separate out the relationships with cluster suppliers and relationships with suppliers based outside the cluster. There was no evidence that personal factors play a more important role in the establishment of within cluster links. In sum, personal networks are important in the identification of within cluster links but they are equally important in the establishment of links outside the cluster. This suggests that the importance of personal interaction within clusters has been overplayed.

Personal Identification Berbasis Web Menggunakan Multinomial Naïve Bayes

Personality is a whole way an individual reacts and interacts with other individuals[1] In this personal identification using the basic theory of Hippocrates. The method used for classification is Multinomial Naïve Bayes. There are several stages of preprocessing such as casefolding, tokenizing with unigram and Bigram well as term frequency. The results of the classification is divided into four categories: sanguine, melancholic, plegmatis and choleric. Confusion matrix is used as evaluation with 85 % accuracy value of 160 training data and 40 testing data consist each category

Detecting Important Life Events on Twitter Using Frequent Semantic and Syntactic Subgraphs

Identifying global events from social media has been the focus of much research in recent years. However, the identification of personal life events poses new requirements and challenges that have received relatively little research attention. In this paper we explore a new approach for life event identification, where we expand social media posts into both semantic, and syntactic networks of content. Frequent graph patterns are mined from these networks and used as features to enrich life-event classifiers. Results show that our approach significantly outperforms the best performing baseline in accuracy (by 4.48% points) and F-measure (by 4.54% points) when used to identify five major life events identified from the psychology literature: Getting Married, Having Children, Death of a Parent, Starting School, and Falling in Love. In addition, our results show that, while semantic graphs are effective at discriminating the theme of the post (e.g. the topic of marriage), syntactic graphs help identify whether the post describes a personal event (e.g. someone getting married)

The Case for National DNA Identification Cards

Foes of the United States have demonstrated their ability to strike at the heart of this country. Fear of renewed attacks and a desire for greater national security have now prompted many to call for improvements in the national personal identification system. In particular, the possibility of a national identification card containing the carrier\u27s DNA information is being seriously considered. However, this raises difficult questions. Would such a card system, and the extraction of individuals\u27 DNA it entails, violate the 4th Amendment of the Constitution? This article will show that such a card system could in fact be found to be constitutional under the law of privacy as it stands today

Life Stories and Mental Health: The Role of Identification Processes in Theory and Interventions

The goal of this article is to explore the relations between narratives and mental health from a psychological perspective. We argue that a process of identification with personal experiences underlies narrative structures that are known to be related to mental health. Overidentification and underidentification are described as general processes underlying mental health problems. Gerontological insights in reminiscence and life review and cognitive psychological studies on autobiographical memories validate this claim. Practical applications in mental health care provide even further evidence for the role of identification processes in mental health and how they can be targeted in intervention