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Dozens of Brazilian universities hit by strikes over academic wages

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Protesters with a large banner outisde the National Education Conference at the University of Brasilia.

Tensions over university funding in Brazil have been simmering for a while. In January, a Lula administration official visited the University of Brasília and was met by protesters asking for better pay.Credit: Leandro Chemalle/Thenews2/Zuma via Alamy

Academic workers, including many professors, are now on strike at more than 60 federal universities across Brazil. Having faced more than a decade of declining budgets, they are demanding higher wages and more funding to pay for crumbling infrastructure, among other things.

Now in their fourth week at some institutions, the strikes have halted classes on many campuses, although scientists contacted by Nature say they are maintaining research laboratories and field projects. The strikes present a major test for President Luiz Inácio Lula da Silva, a long-time labour leader who entered office last year and promised to boost funding for science and education. The Lula administration has unlocked new financing for research grants and fellowships and is rolling out a new — and controversial — initiative to coax thousands of Brazilian scientists who have moved abroad to return home. Salaries remain flat, however, and base funding for universities has continued to fall, owing in part, some scientists say, to opposition from conservative politicians in the Brazilian Congress.

The question of whether to strike has divided academic workers, as well as students, and many professors have opted to keep teaching, even at institutions where their colleagues have voted to walk out. Many of those striking, meanwhile, hope their actions will give the Lula administration some leverage to negotiate a better deal with lawmakers in Brasília.

“We are not against the government,” says Thiago André, a botanist at the University of Brasília. “We are in negotiation with the government.”

Going down

Public funding for higher education in Brazil has generally been decreasing for more than a decade, with significant cuts enacted under Lula’s conservative predecessor, Jair Bolsonaro. Many academics had hoped that the situation would change under Lula, who expanded investments in the federal university system when he was last Brazil’s president, in 2003–11.

Lula has succeeded to some extent in making changes. For instance, his administration has more than doubled the spending from Brazil’s National Fund for the Development of Science and Technology (FNDCT) to 12.8 billion reais (US$2.52 billion) in 2024, says Ricardo Galvão, a physicist who heads the National Council for Scientific and Technological Development, one of the country’s main science agencies. The FNDCT is a core research account in Brazil and is fed by taxes collected from various industries.

Thus far, however, the administration has failed to boost broader funding for universities, including salaries for professors and other staff members.

Since 2014, base funding for federal universities has dropped by around 38%, to an estimated 5.9 billion reais (US$1.16 billion) in 2024, after adjusting for inflation, says Luiz Davidovich, a physicist at the University of Rio de Janeiro, Brazil, and a former president of the Brazilian Academy of Sciences. The result, Davidovich says, is that many universities cannot afford to pay for basic maintenance to keep buildings safe and functional.

For his part, Davidovich joined the majority of professors at his university in voting not to go on strike. “We thought about the students: for them, it would be a disaster” to have their education derailed and graduations delayed, he says. But he sympathizes with those who decided to strike and hopes the situation will raise public awareness and put pressure on the administration and Congress to act.

Galvão acknowledges the challenge ahead. “We need to put much more money in the university system,” he says, but it was perhaps “naive” to expect that the Lula administration would be able to immediately solve the problem.

Continuing talks

It remains unclear how and when the strike might end. One of the the last major strikes by workers at federal universities was in 2015 and lasted around six months. The main union representing professors during the current strike estimates that inflation has reduced the purchasing power of their salaries by around 50% since 2015; union negotiators are asking for a salary increase of nearly 23%. The administration’s latest offer is to boost salaries by 9% in 2025 and another 3.5% in 2026, with no increase this year, according to André.

“It’s not enough,” André says, stressing that professors are not looking for a raise so much as an adjustment for inflation to help them to pay rent and put food on the table. “I think we will stay on strike.”

For botanist Adriana Lobão, the vote to go on strike took place at her institution, Fluminense Federal University in Niteroi, on 29 April. She was conflicted and did not vote, but is now following her colleagues’ decision to strike. Lobão acknowledges that the strike will cause delays in graduation for many students, and others will need to make up for lost time as schedules are readjusted once the strike ends. But she says the situation worsens each year, and thus far most of her students seem supportive of her choice.

“Every day, I hope that the government will make a better offer, so that we can finish this strike,” Lobão says. There’s no end in sight, but she is willing to hold out for now. “I think it’s fair.”

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Three ways ChatGPT helps me in my academic writing

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Jon Gruda

For Dritjon Gruda, artificial-intelligence chatbots have been a huge help in scientific writing and peer review.Credit: Vladimira Stavreva-Gruda

Confession time: I use generative artificial intelligence (AI). Despite the debate over whether chatbots are positive or negative forces in academia, I use these tools almost daily to refine the phrasing in papers that I’ve written, and to seek an alternative assessment of work I’ve been asked to evaluate, as either a reviewer or an editor. AI even helped me to refine this article.

I study personality and leadership at Católica Porto Business School in Portugal and am an associate editor at Personality and Individual Differences and Psychology of Leaders and Leadership. The value that I derive from generative AI is not from the technology itself blindly churning out text, but from engaging with the tool and using my own expertise to refine what it produces. The dialogue between me and the chatbot both enhances the coherence of my work and, over time, teaches me how to describe complex topics in a simpler way.

Whether you’re using AI in writing, editing or peer review, here’s how it can do the same for you.

Polishing academic writing

Ever heard the property mantra, ‘location, location, location’? In the world of generative AI, it’s ‘context, context, context’.

Context is king. You can’t expect generative AI — or anything or anyone, for that matter — to provide a meaningful response to a question without it. When you’re using a chatbot to refine a section of your paper for clarity, start by outlining the context. What is your paper about, and what is your main argument? Jot down your ideas in any format — even bullet points will work. Then, present this information to the generative AI of your choice. I typically use ChatGPT, made by OpenAI in San Francisco, California, but for tasks that demand a deep understanding of language nuances, such as analysing search queries or text, I find Gemini, developed by researchers at Google, to be particularly effective. The open-source large language models made by Mixtral, based in Paris, are ideal when you’re working offline but still need assistance from a chatbot.

Regardless of which generative-AI tool you choose, the key to success lies in providing precise instructions. The clearer you are, the better. For example, you might write: “I’m writing a paper on [topic] for a leading [discipline] academic journal. What I tried to say in the following section is [specific point]. Please rephrase it for clarity, coherence and conciseness, ensuring each paragraph flows into the next. Remove jargon. Use a professional tone.” You can use the same technique again later on, to clarify your responses to reviewer comments.

Remember, the chatbot’s first reply might not be perfect — it’s a collaborative and iterative process. You might need to refine your instructions or add more information, much as you would when discussing a concept with a colleague. It’s the interaction that improves the results. If something doesn’t quite hit the mark, don’t hesitate to say, “This isn’t quite what I meant. Let’s adjust this part.” Or you can commend its improvements: “This is much clearer, but let’s tweak the ending for a stronger transition to the next section.”

This approach can transform a challenging task into a manageable one, filling the page with insights you might not have fully gleaned on your own. It’s like having a conversation that opens new perspectives, making generative AI a collaborative partner in the creative process of developing and refining ideas. But importantly, you are using the AI as a sounding board: it is not writing your document for you; nor is it reviewing manuscripts.

Elevating peer review

Generative AI can be a valuable tool in the peer-review process. After thoroughly reading a manuscript, summarize key points and areas for review. Then, use the AI to help organize and articulate your feedback (without directly inputting or uploading the manuscript’s text, thus avoiding privacy concerns). For example, you might instruct the AI: “Assume you’re an expert and seasoned scholar with 20+ years of academic experience in [field]. On the basis of my summary of a paper in [field], where the main focus is on [general topic], provide a detailed review of this paper, in the following order: 1) briefly discuss its core content; 2) identify its limitations; and 3) explain the significance of each limitation in order of importance. Maintain a concise and professional tone throughout.”

I’ve found that AI partnerships can be incredibly enriching; the tools often offer perspectives I hadn’t considered. For instance, ChatGPT excels at explaining and justifying the reasons behind specific limitations that I had identified in my review, which helps me to grasp the broader implications of the study’s contribution. If I identify methodological limitations, ChatGPT can elaborate on these in detail and suggest ways to overcome them in a revision. This feedback often helps me to connect the dots between the limitations and their collective impact on the paper’s overall contribution. Occasionally, however, its suggestions are off-base, far-fetched, irrelevant or simply wrong. And that is why the final responsibility for the review always remains with you. A reviewer must be able to distinguish between what is factual and what is not, and no chatbot can reliably do that.

Optimizing editorial feedback

The final area in which I benefit from using chatbots is in my role as a journal editor. Providing constructive editorial feedback to authors can be challenging, especially when you oversee several manuscripts every week. Having personally received countless pieces of unhelpful, non-specific feedback — such as, “After careful consideration, we have decided not to proceed with your manuscript” — I recognize the importance of clear and constructive communication. ChatGPT has become indispensable in this process, helping me to craft precise, empathetic and actionable feedback without replacing human editorial decisions.

For instance, after evaluating a paper and noting its pros and cons, I might feed these into ChatGPT and get it to draft a suitable letter: “On the basis of these notes, draft a letter to the author. Highlight the manuscript’s key issues and clearly explain why the manuscript, despite its interesting topic, might not provide a substantial enough advancement to merit publication. Avoid jargon. Be direct. Maintain a professional and respectful tone throughout.” Again, it might take a few iterations to get the tone and content just right.

I’ve found that this approach both enhances the quality of my feedback and helps to guarantee that I convey my thoughts supportively. The result is a more positive and productive dialogue between editors and authors.

There is no doubt that generative AI presents challenges to the scientific community. But it can also enhance the quality of our work. These tools can bolster our capabilities in writing, reviewing and editing. They preserve the essence of scientific inquiry — curiosity, critical thinking and innovation — while improving how we communicate our research.

Considering the benefits, what are you waiting for?

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Students Thrive with Psychology Assignment Help: Academic Success Guaranteed

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