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2981543 Ano: 2023
Disciplina: Inglês (Língua Inglesa)
Banca: FGV
Orgão: Câm. Deputados
Complex societies and the growth of the law
Modern societies rely upon law as the primary mechanism to control their development and manage their conflicts. Through carefully designed rights and responsibilities, institutions and procedures, law can enable humans to engage in increasingly complex social and economic activities. Therefore, law plays an important role in understanding how societies change. To explore the interplay between law and society, we need to study how both co-evolve over time. This requires a firm quantitative grasp of the changes occurring in both domains. But while quantifying societal change has been the subject of tremendous research efforts in fields such as sociology, economics, or social physics for many years, much less work has been done to quantify legal change. In fact, legal scholars have traditionally regarded the law as hardly quantifiable, and although there is no dearth of empirical legal studies, it is only recently that researchers have begun to apply data science methods to law. To date, there have been relatively few quantitative works that explicitly address legal change, and almost no scholarship exists that analyses the time-evolving outputs of the legislative and executive branches of national governments at scale. Unlocking these data sources for the interdisciplinary scientific community will be crucial for understanding how law and society interact.
Our work takes a step towards this goal. As a starting point, we hypothesise that an increasingly diverse and interconnected society might create increasingly diverse and interconnected rules. Lawmakers create, modify, and delete legal rules to achieve particular behavioural outcomes, often in an effort to respond to perceived changes in societal needs. While earlier large-scale quantitative work focused on analysing an individual snapshot of laws enacted by national parliaments, collections of snapshots offer a window into the dynamic interaction between law and society. Such collections represent complete, time-evolving populations of statutes at the national level. Hence, no sampling is needed for their analysis, and all changes we observe are direct consequences of legislative activity. This feature makes collections of nation-level statutes particularly suitable for investigating temporal dynamics.
To preserve the intended multidimensionality of legal document collections and explore how they change over time, legislative corpora should be modelled as dynamic document networks. In particular, since legal documents are carefully organised and interlinked, their structure provides a more direct window into their content and dynamics than their language: Networks honour the deliberate design decisions made by the document authors and circumvent some of the ambiguity problems that natural language-based approaches inherently face. In this paper, we therefore develop an informed data model for legislative corpora, capturing the richness of legislative data for exploration by social physics.
Adapted from Katz, D.M., Coupette, C., Beckedorf, J. et al. Complex societies and the growth of the law. Sci Rep 10, 18737 (2020). Available at https://www.nature.com/articles/s41598-020-73623-x
The word “dearth” in “there is no dearth of empirical legal studies” (1st paragraph) means
 

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Questão presente nas seguintes provas
2981542 Ano: 2023
Disciplina: Inglês (Língua Inglesa)
Banca: FGV
Orgão: Câm. Deputados
Complex societies and the growth of the law
Modern societies rely upon law as the primary mechanism to control their development and manage their conflicts. Through carefully designed rights and responsibilities, institutions and procedures, law can enable humans to engage in increasingly complex social and economic activities. Therefore, law plays an important role in understanding how societies change. To explore the interplay between law and society, we need to study how both co-evolve over time. This requires a firm quantitative grasp of the changes occurring in both domains. But while quantifying societal change has been the subject of tremendous research efforts in fields such as sociology, economics, or social physics for many years, much less work has been done to quantify legal change. In fact, legal scholars have traditionally regarded the law as hardly quantifiable, and although there is no dearth of empirical legal studies, it is only recently that researchers have begun to apply data science methods to law. To date, there have been relatively few quantitative works that explicitly address legal change, and almost no scholarship exists that analyses the time-evolving outputs of the legislative and executive branches of national governments at scale. Unlocking these data sources for the interdisciplinary scientific community will be crucial for understanding how law and society interact.
Our work takes a step towards this goal. As a starting point, we hypothesise that an increasingly diverse and interconnected society might create increasingly diverse and interconnected rules. Lawmakers create, modify, and delete legal rules to achieve particular behavioural outcomes, often in an effort to respond to perceived changes in societal needs. While earlier large-scale quantitative work focused on analysing an individual snapshot of laws enacted by national parliaments, collections of snapshots offer a window into the dynamic interaction between law and society. Such collections represent complete, time-evolving populations of statutes at the national level. Hence, no sampling is needed for their analysis, and all changes we observe are direct consequences of legislative activity. This feature makes collections of nation-level statutes particularly suitable for investigating temporal dynamics.
To preserve the intended multidimensionality of legal document collections and explore how they change over time, legislative corpora should be modelled as dynamic document networks. In particular, since legal documents are carefully organised and interlinked, their structure provides a more direct window into their content and dynamics than their language: Networks honour the deliberate design decisions made by the document authors and circumvent some of the ambiguity problems that natural language-based approaches inherently face. In this paper, we therefore develop an informed data model for legislative corpora, capturing the richness of legislative data for exploration by social physics.
Adapted from Katz, D.M., Coupette, C., Beckedorf, J. et al. Complex societies and the growth of the law. Sci Rep 10, 18737 (2020). Available at https://www.nature.com/articles/s41598-020-73623-x
Based on the text, mark the statements below as true (T) or false (F).

( ) Diachronic studies are required for the study of the interaction between law and society.

( ) The studies of legal change and those of societal change have had an equivalent number of quantitative approaches.

( ) Legal scholars have traditionally applied data science methods to study the history of change.

The statements are, respectively,
 

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2981541 Ano: 2023
Disciplina: Português
Banca: FGV
Orgão: Câm. Deputados
Assinale a opção em que ocorreu uma substituição da voz passiva com o verbo ser pela voz passiva com o pronome se de forma correta.
 

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Questão presente nas seguintes provas

Read Text II and answer the seven questions that follow it

Text II

Boy cries Wolf

After astonishing breakthroughs in artificial intelligence, many people worry that they will end up on the economic scrapheap. Global Google searches for “is my job safe?” have doubled in recent months, as people fear that they will be replaced with large language models (LLMS). Some evidence suggests that widespread disruption is coming. In a recent paper Tyna Eloundou of OpenAI and colleagues say that “around 80% of the US workforce could have at least 10% of their work tasks affected by the introduction of LLMS”. Another paper suggests that legal services, accountancy and travel agencies will face unprecedented upheaval.

Economists, however, tend to enjoy making predictions about automation more than they enjoy testing them. In the early 2010s many of them loudly predicted that robots would kill jobs by the millions, only to fall silent when employment rates across the rich world rose to all-time highs. Few of the doom-mongers have a good explanation for why countries with the highest rates of tech usage around the globe, such as Japan, Singapore and South Korea, consistently have among the lowest rates of unemployment.

Here we introduce our first attempt at tracking AI’s impact on jobs. Using American data on employment by occupation, we single out white-collar workers. These include people working in everything from back-office support and financial operations to copy-writers. White-collar roles are thought to be especially vulnerable to generative AI, which is becoming ever better at logical reasoning and creativity.

However, there is as yet little evidence of an AI hit to employment. In the spring of 2020 white-collar jobs rose as a share of the total, as many people in service occupations lost their job at the start of the covid-19 pandemic. The white-collar share is lower today, as leisure and hospitality have recovered. Yet in the past year the share of employment in professions supposedly at risk from generative AI has risen by half a percentage point.

It is, of course, early days. Few firms yet use generative-AI tools at scale, so the impact on jobs could merely be delayed. Another possibility, however, is that these new technologies will end up destroying only a small number of roles. While AI may be efficient at some tasks, it may be less good at others, such as management and working out what others need.

AI could even have a positive effect on jobs. If workers using it become more efficient, profits at their company could rise which would then allow bosses to ramp up hiring. A recent survey by Experis, an IT-recruitment firm, points to this possibility. More than half of Britain’s employers expect AI technologies to have a positive impact on their headcount over the next two years, it finds.

To see how it all shakes out, we will publish updates to this analysis every few months. But for now, a jobs apocalypse seems a way off.

From The Economist June 17th 2023, p. 71

“as yet” in “there is as yet little evidence” (4th paragraph) can be replaced without significant change of meaning by

 

Provas

Questão presente nas seguintes provas

Read Text II and answer the seven questions that follow it

Text II

Boy cries Wolf

After astonishing breakthroughs in artificial intelligence, many people worry that they will end up on the economic scrapheap. Global Google searches for “is my job safe?” have doubled in recent months, as people fear that they will be replaced with large language models (LLMS). Some evidence suggests that widespread disruption is coming. In a recent paper Tyna Eloundou of OpenAI and colleagues say that “around 80% of the US workforce could have at least 10% of their work tasks affected by the introduction of LLMS”. Another paper suggests that legal services, accountancy and travel agencies will face unprecedented upheaval.

Economists, however, tend to enjoy making predictions about automation more than they enjoy testing them. In the early 2010s many of them loudly predicted that robots would kill jobs by the millions, only to fall silent when employment rates across the rich world rose to all-time highs. Few of the doom-mongers have a good explanation for why countries with the highest rates of tech usage around the globe, such as Japan, Singapore and South Korea, consistently have among the lowest rates of unemployment.

Here we introduce our first attempt at tracking AI’s impact on jobs. Using American data on employment by occupation, we single out white-collar workers. These include people working in everything from back-office support and financial operations to copy-writers. White-collar roles are thought to be especially vulnerable to generative AI, which is becoming ever better at logical reasoning and creativity.

However, there is as yet little evidence of an AI hit to employment. In the spring of 2020 white-collar jobs rose as a share of the total, as many people in service occupations lost their job at the start of the covid-19 pandemic. The white-collar share is lower today, as leisure and hospitality have recovered. Yet in the past year the share of employment in professions supposedly at risk from generative AI has risen by half a percentage point.

It is, of course, early days. Few firms yet use generative-AI tools at scale, so the impact on jobs could merely be delayed. Another possibility, however, is that these new technologies will end up destroying only a small number of roles. While AI may be efficient at some tasks, it may be less good at others, such as management and working out what others need.

AI could even have a positive effect on jobs. If workers using it become more efficient, profits at their company could rise which would then allow bosses to ramp up hiring. A recent survey by Experis, an IT-recruitment firm, points to this possibility. More than half of Britain’s employers expect AI technologies to have a positive impact on their headcount over the next two years, it finds.

To see how it all shakes out, we will publish updates to this analysis every few months. But for now, a jobs apocalypse seems a way off.

From The Economist June 17th 2023, p. 71

In the last sentence of the first paragraph, when the paper mentions an “upheaval”, it refers to the possibility of a future

 

Provas

Questão presente nas seguintes provas

Read Text II and answer the seven questions that follow it

Text II

Boy cries Wolf

After astonishing breakthroughs in artificial intelligence, many people worry that they will end up on the economic scrapheap. Global Google searches for “is my job safe?” have doubled in recent months, as people fear that they will be replaced with large language models (LLMS). Some evidence suggests that widespread disruption is coming. In a recent paper Tyna Eloundou of OpenAI and colleagues say that “around 80% of the US workforce could have at least 10% of their work tasks affected by the introduction of LLMS”. Another paper suggests that legal services, accountancy and travel agencies will face unprecedented upheaval.

Economists, however, tend to enjoy making predictions about automation more than they enjoy testing them. In the early 2010s many of them loudly predicted that robots would kill jobs by the millions, only to fall silent when employment rates across the rich world rose to all-time highs. Few of the doom-mongers have a good explanation for why countries with the highest rates of tech usage around the globe, such as Japan, Singapore and South Korea, consistently have among the lowest rates of unemployment.

Here we introduce our first attempt at tracking AI’s impact on jobs. Using American data on employment by occupation, we single out white-collar workers. These include people working in everything from back-office support and financial operations to copy-writers. White-collar roles are thought to be especially vulnerable to generative AI, which is becoming ever better at logical reasoning and creativity.

However, there is as yet little evidence of an AI hit to employment. In the spring of 2020 white-collar jobs rose as a share of the total, as many people in service occupations lost their job at the start of the covid-19 pandemic. The white-collar share is lower today, as leisure and hospitality have recovered. Yet in the past year the share of employment in professions supposedly at risk from generative AI has risen by half a percentage point.

It is, of course, early days. Few firms yet use generative-AI tools at scale, so the impact on jobs could merely be delayed. Another possibility, however, is that these new technologies will end up destroying only a small number of roles. While AI may be efficient at some tasks, it may be less good at others, such as management and working out what others need.

AI could even have a positive effect on jobs. If workers using it become more efficient, profits at their company could rise which would then allow bosses to ramp up hiring. A recent survey by Experis, an IT-recruitment firm, points to this possibility. More than half of Britain’s employers expect AI technologies to have a positive impact on their headcount over the next two years, it finds.

To see how it all shakes out, we will publish updates to this analysis every few months. But for now, a jobs apocalypse seems a way off.

From The Economist June 17th 2023, p. 71

By calling some economists “doom-mongers” in “Few of the doom-mongers have a good explanation” (2nd paragraph), the authors

 

Provas

Questão presente nas seguintes provas

Read Text II and answer the seven questions that follow it

Text II

Boy cries Wolf

After astonishing breakthroughs in artificial intelligence, many people worry that they will end up on the economic scrapheap. Global Google searches for “is my job safe?” have doubled in recent months, as people fear that they will be replaced with large language models (LLMS). Some evidence suggests that widespread disruption is coming. In a recent paper Tyna Eloundou of OpenAI and colleagues say that “around 80% of the US workforce could have at least 10% of their work tasks affected by the introduction of LLMS”. Another paper suggests that legal services, accountancy and travel agencies will face unprecedented upheaval.

Economists, however, tend to enjoy making predictions about automation more than they enjoy testing them. In the early 2010s many of them loudly predicted that robots would kill jobs by the millions, only to fall silent when employment rates across the rich world rose to all-time highs. Few of the doom-mongers have a good explanation for why countries with the highest rates of tech usage around the globe, such as Japan, Singapore and South Korea, consistently have among the lowest rates of unemployment.

Here we introduce our first attempt at tracking AI’s impact on jobs. Using American data on employment by occupation, we single out white-collar workers. These include people working in everything from back-office support and financial operations to copy-writers. White-collar roles are thought to be especially vulnerable to generative AI, which is becoming ever better at logical reasoning and creativity.

However, there is as yet little evidence of an AI hit to employment. In the spring of 2020 white-collar jobs rose as a share of the total, as many people in service occupations lost their job at the start of the covid-19 pandemic. The white-collar share is lower today, as leisure and hospitality have recovered. Yet in the past year the share of employment in professions supposedly at risk from generative AI has risen by half a percentage point.

It is, of course, early days. Few firms yet use generative-AI tools at scale, so the impact on jobs could merely be delayed. Another possibility, however, is that these new technologies will end up destroying only a small number of roles. While AI may be efficient at some tasks, it may be less good at others, such as management and working out what others need.

AI could even have a positive effect on jobs. If workers using it become more efficient, profits at their company could rise which would then allow bosses to ramp up hiring. A recent survey by Experis, an IT-recruitment firm, points to this possibility. More than half of Britain’s employers expect AI technologies to have a positive impact on their headcount over the next two years, it finds.

To see how it all shakes out, we will publish updates to this analysis every few months. But for now, a jobs apocalypse seems a way off.

From The Economist June 17th 2023, p. 71

If someone ends up “on the economic scrapheap” (1st paragraph), this person will feel

 

Provas

Questão presente nas seguintes provas

Read Text II and answer the seven questions that follow it

Text II

Boy cries Wolf

After astonishing breakthroughs in artificial intelligence, many people worry that they will end up on the economic scrapheap. Global Google searches for “is my job safe?” have doubled in recent months, as people fear that they will be replaced with large language models (LLMS). Some evidence suggests that widespread disruption is coming. In a recent paper Tyna Eloundou of OpenAI and colleagues say that “around 80% of the US workforce could have at least 10% of their work tasks affected by the introduction of LLMS”. Another paper suggests that legal services, accountancy and travel agencies will face unprecedented upheaval.

Economists, however, tend to enjoy making predictions about automation more than they enjoy testing them. In the early 2010s many of them loudly predicted that robots would kill jobs by the millions, only to fall silent when employment rates across the rich world rose to all-time highs. Few of the doom-mongers have a good explanation for why countries with the highest rates of tech usage around the globe, such as Japan, Singapore and South Korea, consistently have among the lowest rates of unemployment.

Here we introduce our first attempt at tracking AI’s impact on jobs. Using American data on employment by occupation, we single out white-collar workers. These include people working in everything from back-office support and financial operations to copy-writers. White-collar roles are thought to be especially vulnerable to generative AI, which is becoming ever better at logical reasoning and creativity.

However, there is as yet little evidence of an AI hit to employment. In the spring of 2020 white-collar jobs rose as a share of the total, as many people in service occupations lost their job at the start of the covid-19 pandemic. The white-collar share is lower today, as leisure and hospitality have recovered. Yet in the past year the share of employment in professions supposedly at risk from generative AI has risen by half a percentage point.

It is, of course, early days. Few firms yet use generative-AI tools at scale, so the impact on jobs could merely be delayed. Another possibility, however, is that these new technologies will end up destroying only a small number of roles. While AI may be efficient at some tasks, it may be less good at others, such as management and working out what others need.

AI could even have a positive effect on jobs. If workers using it become more efficient, profits at their company could rise which would then allow bosses to ramp up hiring. A recent survey by Experis, an IT-recruitment firm, points to this possibility. More than half of Britain’s employers expect AI technologies to have a positive impact on their headcount over the next two years, it finds.

To see how it all shakes out, we will publish updates to this analysis every few months. But for now, a jobs apocalypse seems a way off.

From The Economist June 17th 2023, p. 71

The adjective in “astonishing breakthroughs” (1st paragraph) is similar in meaning to

 

Provas

Questão presente nas seguintes provas

Read Text II and answer the seven questions that follow it

Text II

Boy cries Wolf

After astonishing breakthroughs in artificial intelligence, many people worry that they will end up on the economic scrapheap. Global Google searches for “is my job safe?” have doubled in recent months, as people fear that they will be replaced with large language models (LLMS). Some evidence suggests that widespread disruption is coming. In a recent paper Tyna Eloundou of OpenAI and colleagues say that “around 80% of the US workforce could have at least 10% of their work tasks affected by the introduction of LLMS”. Another paper suggests that legal services, accountancy and travel agencies will face unprecedented upheaval.

Economists, however, tend to enjoy making predictions about automation more than they enjoy testing them. In the early 2010s many of them loudly predicted that robots would kill jobs by the millions, only to fall silent when employment rates across the rich world rose to all-time highs. Few of the doom-mongers have a good explanation for why countries with the highest rates of tech usage around the globe, such as Japan, Singapore and South Korea, consistently have among the lowest rates of unemployment.

Here we introduce our first attempt at tracking AI’s impact on jobs. Using American data on employment by occupation, we single out white-collar workers. These include people working in everything from back-office support and financial operations to copy-writers. White-collar roles are thought to be especially vulnerable to generative AI, which is becoming ever better at logical reasoning and creativity.

However, there is as yet little evidence of an AI hit to employment. In the spring of 2020 white-collar jobs rose as a share of the total, as many people in service occupations lost their job at the start of the covid-19 pandemic. The white-collar share is lower today, as leisure and hospitality have recovered. Yet in the past year the share of employment in professions supposedly at risk from generative AI has risen by half a percentage point.

It is, of course, early days. Few firms yet use generative-AI tools at scale, so the impact on jobs could merely be delayed. Another possibility, however, is that these new technologies will end up destroying only a small number of roles. While AI may be efficient at some tasks, it may be less good at others, such as management and working out what others need.

AI could even have a positive effect on jobs. If workers using it become more efficient, profits at their company could rise which would then allow bosses to ramp up hiring. A recent survey by Experis, an IT-recruitment firm, points to this possibility. More than half of Britain’s employers expect AI technologies to have a positive impact on their headcount over the next two years, it finds.

To see how it all shakes out, we will publish updates to this analysis every few months. But for now, a jobs apocalypse seems a way off.

From The Economist June 17th 2023, p. 71

The title of the article means to

 

Provas

Questão presente nas seguintes provas

Read Text II and answer the seven questions that follow it

Text II

Boy cries Wolf

After astonishing breakthroughs in artificial intelligence, many people worry that they will end up on the economic scrapheap. Global Google searches for “is my job safe?” have doubled in recent months, as people fear that they will be replaced with large language models (LLMS). Some evidence suggests that widespread disruption is coming. In a recent paper Tyna Eloundou of OpenAI and colleagues say that “around 80% of the US workforce could have at least 10% of their work tasks affected by the introduction of LLMS”. Another paper suggests that legal services, accountancy and travel agencies will face unprecedented upheaval.

Economists, however, tend to enjoy making predictions about automation more than they enjoy testing them. In the early 2010s many of them loudly predicted that robots would kill jobs by the millions, only to fall silent when employment rates across the rich world rose to all-time highs. Few of the doom-mongers have a good explanation for why countries with the highest rates of tech usage around the globe, such as Japan, Singapore and South Korea, consistently have among the lowest rates of unemployment.

Here we introduce our first attempt at tracking AI’s impact on jobs. Using American data on employment by occupation, we single out white-collar workers. These include people working in everything from back-office support and financial operations to copy-writers. White-collar roles are thought to be especially vulnerable to generative AI, which is becoming ever better at logical reasoning and creativity.

However, there is as yet little evidence of an AI hit to employment. In the spring of 2020 white-collar jobs rose as a share of the total, as many people in service occupations lost their job at the start of the covid-19 pandemic. The white-collar share is lower today, as leisure and hospitality have recovered. Yet in the past year the share of employment in professions supposedly at risk from generative AI has risen by half a percentage point.

It is, of course, early days. Few firms yet use generative-AI tools at scale, so the impact on jobs could merely be delayed. Another possibility, however, is that these new technologies will end up destroying only a small number of roles. While AI may be efficient at some tasks, it may be less good at others, such as management and working out what others need.

AI could even have a positive effect on jobs. If workers using it become more efficient, profits at their company could rise which would then allow bosses to ramp up hiring. A recent survey by Experis, an IT-recruitment firm, points to this possibility. More than half of Britain’s employers expect AI technologies to have a positive impact on their headcount over the next two years, it finds.

To see how it all shakes out, we will publish updates to this analysis every few months. But for now, a jobs apocalypse seems a way off.

From The Economist June 17th 2023, p. 71

Based on Text II, mark the statements below as TRUE (T) or FALSE (F).

( ) Many believe AI will eventually make jobs redundant.

( ) The conclusion of the text is that the current outlook regarding employment is rather bleak.

( ) The authors prefer to probe forthcoming evidence before issuing unequivocal accounts.

The statements are, respectively,

 

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