What’s in an Occupation? Investigating Within-Occupation Variation and Gender Segregation Using Job Titles and Task Descriptions

Occupations have long been central to the study of inequality and mobility. However, the occupational categories typical in most U.S. survey data conceal potentially important patterns within occupations. This project uses a novel data source that has not previously been released for analysis: the verbatim text responses provided by respondents to the General Social Survey from 1972 to 2018 when asked about their occupation. These text data allow for an investigation of variation within occupations, in terms of job titles and task descriptions, and the occupation-level factors associated with this variation. I construct an index of occupational similarity based on the average pairwise cosine similarity between job titles and between task descriptions within occupations. Findings indicate substantial variation in the level of similarity across occupations. Occupational prestige, education, and income are associated with less heterogeneity in terms of job titles but slightly more heterogeneity in terms of task descriptions. Gender diversity is associated with more internal heterogeneity in terms of both job titles and task descriptions. In addition, I use the case of gender segregation to demonstrate how occupational categories can conceal the depth and form of stratification.

The impact of task description linguistic style on task performance: a text mining of crowdsourcing contests

PurposeExisting studies on the relationship between task description and task performance are insufficient, with many studies considering description length rather than content to measure quality or only evaluating a single aspect of task performance. To address this gap, this study analyzes the linguistic styles of task descriptions from 2,545 tasks on the Taskcn.com crowdsourcing platform.Design/methodology/approachAn empirical analysis was completed for task description language styles and task performance. The paper used text mining tool Simplified Chinese Linguistic Inquiry and Word Count to extract eight linguistic styles, namely readability, self-distancing, cognitive complexity, causality, tentative language, humanizing personal details, normative information and language intensity. And it tests the relationship between the eight language styles and task performance.FindingsThe study found that more cognitive complexity markers, tentative language, humanized details and normative information increase the quantity of submissions for a task. In addition, more humanized details and normative information in a task description improves the quality of task. Conversely, the inclusion of more causal relationships in a task description reduces the quantity of submissions. Poorer readability of the task description, less self-estrangement and higher language intensity reduces the quality of the task.Originality/valueThis study first reveals the importance of the linguistic styles used in task descriptions and provides a reference for how to attract more task solvers and achieve higher quality task performance by improving task descriptions. The research also enriches existing knowledge on the impact of linguistic styles and the applications of text mining.

What’s in an Occupation? Investigating Within-Occupation Variation and Gender Segregation Using Job Titles and Task Descriptions

Occupations have long been central to the study of inequality and mobility. However, the occupational categories typical in most U.S. survey data conceal potentially important patterns within occupations. This project uses a novel data source that has not previously been released for analysis: the verbatim text responses provided by respondents to the General Social Survey from 1972 to 2018 when asked about their occupation. These text data allow for an investigation of variation within occupations, in terms of job titles and task descriptions, and the occupation-level factors associated with this variation. I construct an index of occupational similarity based on the average pairwise cosine similarity between job titles and between task descriptions within occupations. Findings indicate substantial variation in the level of similarity across occupations. Occupational prestige, education, and income are associated with less heterogeneity in terms of job titles but slightly more heterogeneity in terms of task descriptions. Gender diversity is associated with more internal heterogeneity in terms of both job titles and task descriptions. In addition, I use the case of gender segregation to demonstrate how occupational categories can conceal the depth and form of stratification.

Assessing pragmatic competence in oral proficiency interviews at the C1 level with the new CEFR descriptors

The study of pragmatic competence has gained increasing importance within second language assessment over the last three decades. However, its study in L2 language testing is still scarce. The aim of this paper is to research the extent to which pragmatic competence as defined by the Common European Framework of Reference for Languages (CEFR) has been accommodated in the task descriptions and rating scales of two of the most popular Oral Proficiency Interviews (OPIs) at a C1 level: Cambridge’s Certificate in Advanced English (CAE) and Trinity’s Integrated Skills in English (ISE) III. To carry out this research, OPI tests are first defined, highlighting their differences from L2 pragmatic tests. After pragmatic competence in the CEFR is examined, focusing on the updates in the new descriptors, CAE and ISE III formats, structure and task characteristics are compared, showing that, while the formats and some characteristics are found to differ, the structures and task types are comparable. Finally, we systematically analyse CEFR pragmatic competence in the task skills and rating scale descriptors of both OPIs. The findings show that the task descriptions incorporate mostly aspects of discourse and design competence. Additionally, we find that each OPI is seen to prioritise different aspects of pragmatic competence within their rating scale, with CAE focusing mostly on discourse competence and fluency, and ISE III on functional competence. Our study shows that the tests fail to fully accommodate all aspects of pragmatic competence in the task skills and rating scales, although the aspects they do incorporate follow the CEFR descriptors on pragmatic competence. It also reveals a mismatch between the task competences being tested and the rating scale. To conclude, some research lines are proposed.

Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer

Knowledge transfer between tasks can improve the performance of learned models, but requires an accurate estimate of inter-task relationships to identify the relevant knowledge to transfer. These inter-task relationships are typically estimated based on training data for each task, which is inefficient in lifelong learning settings where the goal is to learn each consecutive task rapidly from as little data as possible. To reduce this burden, we develop a lifelong learning method based on coupled dictionary learning that utilizes high-level task descriptions to model inter-task relationships. We show that using task descriptors improves the performance of the learned task policies, providing both theoretical justification for the benefit and empirical demonstration of the improvement across a variety of learning problems. Given only the descriptor for a new task, the lifelong learner is also able to accurately predict a model for the new task through zero-shot learning using the coupled dictionary, eliminating the need to gather training data before addressing the task.

Towards Task Understanding in Visual Settings

We consider the problem of understanding real world tasks depicted in visual images. While most existing image captioning methods excel in producing natural language descriptions of visual scenes involving human tasks, there is often the need for an understanding of the exact task being undertaken rather than a literal description of the scene. We leverage insights from real world task understanding systems, and propose a framework composed of convolutional neural networks, and an external hierarchical task ontology to produce task descriptions from input images. Detailed experiments highlight the efficacy of the extracted descriptions, which could potentially find their way in many applications, including image alt text generation.