Knowledge Structure for Acquiring Personal Taste Information

2003 ◽  
pp. 454-460 ◽  
Author(s):  
Kazuya Mera ◽  
Takumi Ichimura
Keyword(s):  
Knowledge Structure ◽  
Personal Taste

An Epistemological Criticism of Social Entrepreneurship: Is Social Entrepreneurship a Sound Scientific Field of Research

2018 ◽  
Author(s):  
Amir Forouharfar
Keyword(s):  
Social Entrepreneurship ◽  
Social Constructivism ◽  
Social Epistemology ◽  
American English ◽  
Meta Analysis ◽  
Research Question ◽  
Knowledge Structure ◽  
Scientific Field ◽  
The Other ◽  
First Time

The paper was shaped around the pivotal question: Is SE a sound and scientific field of research? The question has given a critical tone to the paper and has also helped to bring out some of the controversial debates in the realm of SE. The paper was organized under five main discussions to be able to provide a scientific answer to the research question: (1)<b> </b>is “social entrepreneurship” an oxymoron?, (2) the characteristics of SE knowledge, (3) sources of social entrepreneurship knowledge, (4) SE knowledge: structure and limitations and (5) contributing epistemology-making concepts for SE.<b> </b>Based on the sections,<b> </b>the study relied on the relevant philosophical schools of thought in <i>Epistemology </i>(e.g. <i>Empiricism</i>, <i>Rationalism</i>, <i>Skepticism</i>, <i>Internalism</i> vs. <i>Externalism</i>,<i> Essentialism, Social Constructivism</i>, <i>Social Epistemology, etc.</i>) to discuss these controversies around SE and proposes some solutions by reviewing SE literature. Also, to determine the governing linguistic discourse in the realm of SE, which was necessary for our discussion,<i> Corpus of Contemporary American English (COCA)</i> for the first time in SE studies was used. Further, through the study, SE buzzwords which constitute SE terminology were derived and introduced to help us narrowing down and converging the thoughts in this field and demarking the epistemological boundaries of SE. The originality of the paper on one hand lies in its pioneering discussions on SE epistemology and on the other hand in paving the way for a construction of sound epistemology for SE; therefore in many cases after preparing the philosophical ground for the discussions, it went beyond the prevalent SE literature through meta-analysis to discuss the cases which were raised. The results of the study verified previously claimed embryonic pre-paradigmatic phase in SE which was far from a sound and scientific knowledge, although the scholarly endeavors are the harbingers of such a possibility in the future which calls for further mature academic discussion and development of SE knowledge by the SE academia.

scholarly journals A Sensibilist Explanation of Imaginative Resistance

2021 ◽  
pp. 1-16
Author(s):  
Nils Franzén
Keyword(s):  
Actual World ◽  
David Hume ◽  
Recent Discussion ◽  
Personal Taste ◽  
Predicates Of Personal Taste ◽  
Imaginative Resistance ◽  
The World ◽  
The Right ◽  
Evaluative Terms ◽  
The Way

Abstract This article discusses why it is the case that we refuse to accept strange evaluative claims as being true in fictions, even though we are happy to go along with other types of absurdities in such contexts. For instance, we would refuse to accept the following statement as true, even in the context of a fiction: (i) In killing her baby, Giselda did the right thing; after all, it was a girl. This article offers a sensibilist diagnosis of this puzzle, inspired by an observation first made by David Hume. According to sensibilism, the way we feel about things settles their evaluative properties. Thus, when confronted with a fictional scenario where the configuration of non-evaluative facts and properties is relevantly similar to the actual world, we refuse to go along with evaluative properties being instantiated according to a different pattern. It is the attitudes we hold in the actual world that fix the extension of evaluative terms, even in nonactual worlds. When engaging with a fiction, we (to some extent) leave our beliefs about what the world is like behind, while taking our emotional attitudes with us into the fiction. To substantiate this diagnosis, this paper outlines a sensibilist semantics for evaluative terms based on recent discussion regarding predicates of personal taste, and explains how, together with standard assumptions about the nature of fictional discourse, it makes the relevant predictions with respect to engagement with fictions.

scholarly journals Joint regression and learning from pairwise rankings for personalized image aesthetic assessment

2021 ◽  
Author(s):  
Jin Zhou ◽  
Qing Zhang ◽  
Jian-Hao Fan ◽  
Wei Sun ◽  
Wei-Shi Zheng
Keyword(s):  
Large Scale ◽  
Assessment Model ◽  
Generic Model ◽  
Small Subset ◽  
Deep Convolutional Neural Networks ◽  
Personal Taste ◽  
Hinge Loss ◽  
Novel Approach ◽  
Large Scale Dataset ◽  
Image Pairs

AbstractRecent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks (CNNs). However, these methods focus primarily on predicting generally perceived preference of an image, making them usually have limited practicability, since each user may have completely different preferences for the same image. To address this problem, this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste. We achieve this in a coarse to fine manner, by joint regression and learning from pairwise rankings. Specifically, we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs. We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores, and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression. Next, we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss. Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences, clearly outperforming state-of-the-art methods. Moreover, we show that the learned personalized image aesthetic benefits a wide variety of applications.

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