Using Language Processing to Evaluate the Equivalency of the FASB and IASB Standards

2016 ◽  
Vol 13 (2) ◽  
pp. 129-144 ◽  
Author(s):  
Ingrid E. Fisher ◽  
Robert A. Nehmer
Keyword(s):  
Conceptual Framework ◽  
Language Processing ◽  
Financial Statements ◽  
The United States ◽  
United States Congress ◽  
Balance Sheets ◽  
Semantic Richness ◽  
Data Transparency ◽  
Open Question ◽  
Lexical Content

ABSTRACT The passage of the Data Transparency and Accountability Act in the United States Congress will necessitate that government agencies provide more data in transparent formats. The issue of how to interpret such data remains an open question. The accounting profession has continued to struggle with common formats since the inception of balance sheets and income statements. The original FASB Conceptual Framework was developed to help construct consistent GAAP standards. XBRL was developed to provide a consistent representation of the data contained in financial statements and other financial documents. This research explores the use of two codifications (U.S. GAAP and IFRS) of GAAP standards in both their syntactic representation through XBRL taxonomies and their semantics through their authoritative references back to their own standards and codification. The research uses language theory to model the codifications in terms of the strings used to represent lexical content in the financial statements and to provide a systematic mapping to the semantics of the related XBRL specifications. The immediate objectives of this research are to provide a means to compare the semantic richness of U.S. GAAP and IFRS and to determine the consistency of either standardization with respect to the emerging shared Conceptual Framework. Ultimately, to the extent that the system is able to model both the syntax and the semantics of the financial statements, it could provide a baseline on which to consider assurance over parts of the financial statements, rather than over the financial statements taken as a whole.

The Conceptual Framework and Accounting for Leases

2001 ◽  
Vol 15 (3) ◽  
pp. 275-287 ◽  
Author(s):  
Dennis W. Monson
Keyword(s):  
Conceptual Framework ◽  
Decision Model ◽  
Accounting Standards ◽  
Financial Statements ◽  
Financial Accounting ◽  
Financial Accounting Standards Board ◽  
Balance Sheets ◽  
Physical Plant ◽  
Lease Contracts ◽  
Lease Accounting

For years, users of financial statements, academics, and standards setters alike have criticized the lease accounting standards as unnecessarily complex and ineffective in portraying liabilities arising from lease contracts in the balance sheets of lessee enterprises. Recognizing that current standards were adopted before the Financial Accounting Standards Board (FASB) and other standard-setting bodies completed their conceptual framework projects, critics of the lease accounting standards contend that the principal defect in existing standards is that they are at variance with the definitions of assets and liabilities in those frameworks. Some, including the Chairman and several other charter members of the newly formed International Accounting Standards Board (IASB), have called for new lease accounting standards anchored securely in the framework definitions of assets and liabilities. There is not universal agreement, however, on exactly what assets and liabilities result from applying these definitions to a lease contract. For companies that lease a significant amount of physical plant, financial statements produced under the two alternative interpretations explored in this paper are radically different. This paper proposes a decision model for choosing between two alternative interpretations of the definitions of assets and liabilities in a leasing context, illustrates the effects on the basic financial statements of a lessee enterprise of applying these two alternative interpretations, and evaluates the results using the proposed decision model.

From Start-Up to Scale-Up

2018 ◽  
Author(s):  
Gilles Duruflé ◽  
Thomas Hellmann ◽  
Karen Wilson
Keyword(s):  
United States ◽  
Conceptual Framework ◽  
Public Policies ◽  
Scale Up ◽  
The United States ◽  
Smart Money ◽  
Start Up ◽  
The Us ◽  
The Creation

This chapter examines the challenge for entrepreneurial companies of going beyond the start-up phase and growing into large successful companies. We examine the long-term financing of these so-called scale-up companies, focusing on the United States, Europe, and Canada. The chapter first provides a conceptual framework for understanding the challenges of financing scale-ups. It emphasizes the need for investors with deep pockets, for smart money, for investor networks, and for patient money. It then shows some data about the various aspects of financing scale-ups in the United States, Europe, and Canada, showing how Europe and Canada are lagging behind the US relatively more at the scale-up than the start-up stage. Finally, the chapter raises the question of long-term public policies for supporting the creation of a better scale-up environment.

Race and Gender

2020 ◽  
pp. 251-269 ◽  
Author(s):  
Timnit Gebru
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
The United States ◽  
Error Rates ◽  
Political Factors ◽  
Recidivism Rates ◽  
Race And Gender ◽  
Decision Tools ◽  
And Gender ◽  
Technical Solutions

This chapter discusses the role of race and gender in artificial intelligence (AI). The rapid permeation of AI into society has not been accompanied by a thorough investigation of the sociopolitical issues that cause certain groups of people to be harmed rather than advantaged by it. For instance, recent studies have shown that commercial automated facial analysis systems have much higher error rates for dark-skinned women, while having minimal errors on light-skinned men. Moreover, a 2016 ProPublica investigation uncovered that machine learning–based tools that assess crime recidivism rates in the United States are biased against African Americans. Other studies show that natural language–processing tools trained on news articles exhibit societal biases. While many technical solutions have been proposed to alleviate bias in machine learning systems, a holistic and multifaceted approach must be taken. This includes standardization bodies determining what types of systems can be used in which scenarios, making sure that automated decision tools are created by people from diverse backgrounds, and understanding the historical and political factors that disadvantage certain groups who are subjected to these tools.

scholarly journals Development and validation of the Readiness to Train Assessment Tool (RTAT)

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ianita Zlateva ◽  
Amanda Schiessl ◽  
Nashwa Khalid ◽  
Kerry Bamrick ◽  
Margaret Flinter
Keyword(s):  
United States ◽  
Factor Analysis ◽  
Conceptual Framework ◽  
Health Center ◽  
Assessment Tool ◽  
Training Programs ◽  
Health Professions ◽  
Internal Consistency Reliability ◽  
The United States ◽  
Health Centers

Abstract Background In recent years, health centers in the United States have embraced the opportunity to train the next generation of health professionals. The uniqueness of the health centers as teaching settings emphasizes the need to determine if health professions training programs align with health center priorities and the nature of any adjustments that would be needed to successfully implement a training program. We sought to address this need by developing and validating a new survey that measures organizational readiness constructs important for the implementation of health professions training programs at health centers where the primary role of the organizations and individuals is healthcare delivery. Methods The study incorporated several methodological steps for developing and validating a measure for assessing health center readiness to engage with health professions programs. A conceptual framework was developed based on literature review and later validated by 20 experts in two focus groups. A survey-item pool was generated and mapped to the conceptual framework and further refined and validated by 13 experts in three modified Delphi rounds. The survey items were pilot-tested with 212 health center employees. The final survey structure was derived through exploratory factor analysis. The internal consistency reliability of the scale and subscales was evaluated using Chronbach’s alpha. Results The exploratory factor analysis revealed a 41-item, 7-subscale solution for the survey structure, with 72% of total variance explained. Cronbach’s alphas (.79–.97) indicated high internal consistency reliability. The survey measures: readiness to engage, evidence strength and quality of the health professions training program, relative advantage of the program, financial resources, additional resources, implementation team, and implementation plan. Conclusions The final survey, the Readiness to Train Assessment Tool (RTAT), is theoretically-based, valid and reliable. It provides an opportunity to evaluate health centers’ readiness to implement health professions programs. When followed with appropriate change strategies, the readiness evaluations could make the implementation of health professions training programs, and their spread across the United States, more efficient and cost-effective. While developed specifically for health centers, the survey may be useful to other healthcare organizations willing to assess their readiness to implement education and training programs.

scholarly journals A Study of the Effects of the COVID-19 Pandemic on the Experience of Back Pain Reported on Twitter® in the United States: A Natural Language Processing Approach

2021 ◽  
Vol 18 (9) ◽  
pp. 4543
Author(s):  
Krzysztof Fiok ◽  
Waldemar Karwowski ◽  
Edgar Gutierrez ◽  
Maham Saeidi ◽  
Awad M. Aljuaid ◽  
...  
Keyword(s):  
United States ◽  
Natural Language Processing ◽  
Back Pain ◽  
Natural Language ◽  
Language Processing ◽  
The United States ◽  
Daily Routine ◽  
Body Movements ◽  
Data Source ◽  
Twitter Users

The COVID-19 pandemic has changed our lifestyles, habits, and daily routine. Some of the impacts of COVID-19 have been widely reported already. However, many effects of the COVID-19 pandemic are still to be discovered. The main objective of this study was to assess the changes in the frequency of reported physical back pain complaints reported during the COVID-19 pandemic. In contrast to other published studies, we target the general population using Twitter as a data source. Specifically, we aim to investigate differences in the number of back pain complaints between the pre-pandemic and during the pandemic. A total of 53,234 and 78,559 tweets were analyzed for November 2019 and November 2020, respectively. Because Twitter users do not always complain explicitly when they tweet about the experience of back pain, we have designed an intelligent filter based on natural language processing (NLP) to automatically classify the examined tweets into the back pain complaining class and other tweets. Analysis of filtered tweets indicated an 84% increase in the back pain complaints reported in November 2020 compared to November 2019. These results might indicate significant changes in lifestyle during the COVID-19 pandemic, including restrictions in daily body movements and reduced exposure to routine physical exercise.

Nursing Science, Mental Illness, and the Family: A Conceptual Framework to Break the Cycle of Suffering

2020 ◽  
Vol 34 (1) ◽  
pp. 59-66
Author(s):  
Susan M. Hunter Revell ◽  
Mary K. McCurry
Keyword(s):  
Mental Illness ◽  
Conceptual Framework ◽  
Family Health ◽  
The United States ◽  
System Level ◽  
Community Resources ◽  
Nursing Science ◽  
The Family ◽  
Integral Role ◽  
Integrated Programs

Mental illness is an epidemic in the United States, and there is a gap in care due to minimal integrated programs and transitional community resources. This paper reports the development of a conceptual framework to identify challenges facing families living with mental illness and the integral role nursing plays to positively impact health. An inductive, bottom-up approach was used to develop the Nursing Science, Mental Illness and Family model. Concepts clustered around family health, cycle of suffering, improving outcomes, healthcare policy, and nursing science. Successful, goal-directed interprofessional collaborations are essential for individual-, family-, and system-level interventions to be effective.

The 3 C's of the Circular Economy for Food. A conceptual framework for circular design in the food system

2021 ◽  
Author(s):  
Franco Fassio
Keyword(s):  
Sustainable Development ◽  
Conceptual Framework ◽  
Circular Economy ◽  
Food System ◽  
Raw Material ◽  
Starting Point ◽  
Development Goals ◽  
Definition Of ◽  
Open Question ◽  
Circular Design

Food, the basic connecting unit of all the UN's Sustainable Development Goals, plays a crucial role in the ecological transition towards a circular economic paradigm. This paper takes scientific considerations as a starting point in order to contribute to the definition of a theoretical-operational framework in which to grow the Circular Economy for Food. This is a still-open question in a sector of the circular economy that is emerging as vital to sustainable development. The 3 C's of Capital, Cyclicality and Co-evolution offer a systemic, holistic vision of the food system's role. Within this conceptual framework, the designers can find the main boundaries of the system, within which to express their creativity. The aim must be to avoid damaging relationships with the best supplier of raw material known to humanity (Nature), respecting planetary boundaries and at the same time offering a fair space to civil society.

A Natural-Language-Processing-Based Procedure for Generating Distractors for Multiple-Choice Questions

2021 ◽  
pp. 016327872110469
Author(s):  
Peter Baldwin ◽  
Janet Mee ◽  
Victoria Yaneva ◽  
Miguel Paniagua ◽  
Jean D’Angelo ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Multiple Choice ◽  
Incorrect Response ◽  
The United States ◽  
Choice Test ◽  
Multiple Choice Questions ◽  
Response Options ◽  
Medical Licensing

One of the most challenging aspects of writing multiple-choice test questions is identifying plausible incorrect response options—i.e., distractors. To help with this task, a procedure is introduced that can mine existing item banks for potential distractors by considering the similarities between a new item’s stem and answer and the stems and response options for items in the bank. This approach uses natural language processing to measure similarity and requires a substantial pool of items for constructing the generating model. The procedure is demonstrated with data from the United States Medical Licensing Examination (USMLE®). For about half the items in the study, at least one of the top three system-produced candidates matched a human-produced distractor exactly; and for about one quarter of the items, two of the top three candidates matched human-produced distractors. A study was conducted in which a sample of system-produced candidates were shown to 10 experienced item writers. Overall, participants thought about 81% of the candidates were on topic and 56% would help human item writers with the task of writing distractors.

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