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The Chinese Current Account Imbalances: Puzzles, Patterns, and Possible Causes

Chang Ma, Shang-Jin Wei

NBER Working Paper·2026

China's large current account surplus has been an irritant to its trading partners. While industrial and trade policies often lead to sector-level imbalances, they play a relatively limited role in the economy-wide surplus. Structural factors such as an unbalanced sex ratio and uneven access to financing by state-owned and non-state firms are more important determinants of the current account imbalance. While macroeconomic stimulus can boost imports and reduce the surplus in the short run, any long-term solution would need to involve reforms aiming at addressing the structural problems.

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I knew my writing students were using AI. Their confessions led to a powerful teaching moment | AI (artificial intelligence) | The Guardian

Guardian staff reporter

the Guardian·2026

The problem wasn’t just the perfectly polished, yet mediocre prose. It’s what’s lost when we surrender the struggle to translate thought into words

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Suffering Peacefully: Experiences of Infancy Death in Contemporary Zambia

Sesilie Smørholm

Ethos·2016·8 citations

AbstractIn Ng'ombe Township in Lusaka, the death of a baby is often met with silence. Based on long‐term ethnographic fieldwork, this article explores how the bereaved mother's silence is guided by wider cultural norms and values associated with death, by complex notions of what it means to be a person, and by local perceptions of mental health and well‐being. To enhance the complexity of the mother's silences, it also explores how structures of poverty manifest in mothers’ experiences of loss and how silence may hold feelings of inadequacy but also of care and compassion. Finally, the article aims to provide a counterweight to the predominant assumption that mothers in poor communities, who experience high levels of infant mortality, fail to mourn the death of their babies, as well as to psychological theories that assumes verbal expressions as vital for the mourner's mental recovery after loss.

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The Importance of Being Orgasmic: Sexuality, Gender, and Marital Sex Manuals in the United States, 1920-1963

Jessamyn Neuhaus

Journal of the History of Sexuality·2000

No abstract available

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Essay: Art created by psychiatric patients

Mia Lejsted, Johannes Nielsen

The Lancet·2006·3 citations

No abstract available

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The Identity of Psychiatry and the Challenge of Mad Activism: Rethinking the Clinical Encounter

Mohammed Abouelleil Rashed

The Journal of Medicine and Philosophy: A Forum for Bioethics and Philosophy of Medicine·2020·12 citations

Central to the identity of modern medical specialities, including psychiatry, is the notion of hypostatic abstraction: doctors treat conditions or disorders, which are conceived of as “things” that people “have.” Mad activism rejects this notion and hence challenges psychiatry’s identity as a medical specialty. This article elaborates the challenge of Mad activism and develops the hypostatic abstraction as applied to medicine. For psychiatry to maintain its identity as a medical speciality while accommodating the challenge of Mad activism, it must develop an additional conception of the clinical encounter. Toward elaborating this conception, this article raises two basic framing questions: For what kind of understanding of the situation should the clinical encounter aim? What is the therapeutic aim of the encounter as a whole? It proposes that the concepts of “secondary insight” (as the aim of understanding) and of “identity-making” (as a therapeutic aim) can allow the clinical encounter to proceed in a way that accommodates the challenge of Mad activism.

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I-DLM: Introspective Diffusion Language Models

Yifan Yu, Yuqing Jian, Junxiong Wang, Zhongzhu Zhou, Donglin Zhuang, Xinyu Fang, Sri Yanamandra, Xiaoxia Wu, Qingyang Wu, Shuaiwen Leon Song, Tri Dao, Ben Athiwaratkun, James Zou, Fan Lai, Chenfeng Xu

introspective-diffusion.github.io

Diffusion language models (DLMs) offer a compelling promise: parallel token generation could break the sequential bottleneck of autoregressive (AR) decoding. Yet in practice, DLMs consistently lag behind AR models in quality. We argue that this gap stems from a fundamental failure of introspective consistency: AR models agree with what they generate, whereas DLMs often do not. We introduce the Introspective Diffusion Language Model (I-DLM), which uses introspective strided decoding (ISD) to verify previously generated tokens while advancing new ones in the same forward pass. Empirically, I-DLM-8B is the first DLM to match the quality of its same-scale AR counterpart, outperforming LLaDA-2.1-mini (16B) by +26 on AIME-24 and +15 on LiveCodeBench-v6 with half the parameters, while delivering 2.9-4.1x throughput at high concurrency. With gated LoRA, ISD enables bit-for-bit lossless acceleration.

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A closer look at how large language models ‘trust' humans: patterns and biases

Valeria Lerman, Yaniv Dover

Proceedings of the Royal Society A Mathematical Physical and Engineering Science·2026

Abstract As large language models (LLMs) and LLM-based agents increasingly interact with humans in decision-making contexts, understanding trust dynamics between humans and AI agents becomes crucial. While human trust in AI is well-studied, how LLMs develop emulated trust in humans remains far less understood. We compare five LLMs with human participants across five scenarios and 43 200 simulations where subjects vary in competence, benevolence, integrity, and demographics. LLMs and humans show partial convergence: both reliably distinguish high- from low-trait levels, and higher competence and integrity yield favourable decisions, suggesting a shared basic structure of trust. However, LLMs are significantly more extreme and internally consistent, and the basic trust model explains much more variance in their decisions than in humans'. LLMs also treat the three dimensions as more independent, whereas humans collapse them into a global ‘good person' impression. Within the models themselves, we observe substantial heterogeneity in how trust is emulated. LLMs also exhibit stronger, more systematic demographic biases than humans. Overall, LLMs seem to implement a coherent but rigid and sometimes biased model of interpersonal trust that only partially aligns with human judgment, highlighting both their potential and their limitations as decision-support tools and as models of human social cognition.

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Newest and Lowest Rated Papers

The Chinese Current Account Imbalances: Puzzles, Patterns, and Possible Causes

I knew my writing students were using AI. Their confessions led to a powerful teaching moment | AI (artificial intelligence) | The Guardian

Suffering Peacefully: Experiences of Infancy Death in Contemporary Zambia

The Importance of Being Orgasmic: Sexuality, Gender, and Marital Sex Manuals in the United States, 1920-1963

Essay: Art created by psychiatric patients

The Identity of Psychiatry and the Challenge of Mad Activism: Rethinking the Clinical Encounter

I-DLM: Introspective Diffusion Language Models

Agents of Chaos

Does your boss have AI brain?

Matrilineal Subjects: Ambiguity, Bodies, and Metamorphosis in the Fourth Branch of the<i>Mabinogi</i>

Everything's Fine

A closer look at how large language models ‘trust' humans: patterns and biases