Early Childhood Teacher Education (ECTE) is shaped by historical legacies of racism and white supremacy that continue to structure pedagogical norms, curricular priorities, and professional expectations. These legacies are often reproduced through developmentalist and ostensibly neutral approaches that obscure power, normalize anti-Blackness, and reinforce deficit-based narratives of children, families, and educators. Drawing on Critical Race Theory (CRT) and counternarrative methodology, this article presents counterstories from our experiences as Asian American (AA) women teacher educators working across community college and university contexts. We analyze our pedagogical practices alongside student reflections, course artifacts, and institutional interactions to examine how justice-centered teaching is enacted, constrained, and contested within ECTE. Our analysis identifies three intersecting areas of pedagogical disruption: (1) cultivating relational and collective care cultures, (2) redistributing power through assessment and participation practices, and (3) naming and addressing racialized and oppressive realities through critical engagement, imagination, and action. Together, these disruptions challenge dominant Eurocentric logics and unsettle claims of neutrality in teacher education. By centering students' lived experiences, community knowledge, and cultural wealth, this study demonstrates how counterstorytelling and justice-centered pedagogy can function as sites of resistance, possibility, and collective responsibility for systemic transformation in ECTE.
Pulsar timing arrays (PTAs) have recently entered the detection era, quickly moving beyond the goal of simply improving sensitivity at the lowest frequencies for the sake of observing the stochastic gravitational wave background (GWB), and focusing on its accurate spectral characterization. While all PTA collaborations around the world use Fourier-domain Gaussian processes to model the GWB and intrinsic long time-correlated (red) noise, techniques to model the time-correlated radio-frequency-dependent (chromatic) processes have varied from collaboration to collaboration. Here we test a new class of models for PTA data, Gaussian processes based on time-domain kernels that model the statistics of the chromatic processes starting from the covariance matrix. As we will show, these models can be effectively equivalent to Fourier-domain models in mitigating chromatic noise. This work presents a method for Bayesian model selection across the various choices of kernel as well as deterministic chromatic models for nonstationary chromatic events and the solar wind. As PTAs turn toward high frequency (>1 yr(-1)) sensitivity, the size of the basis used to model these processes will need to increase, and these time-domain models present some computational efficiencies compared to Fourier-domain models.
The rapid expansion of the Internet of Things (IoT) ecosystem has propelled widespread deployment of distributed low-power wireless networks, among which ZigBee stands out for diverse innovative applications. However, energy-efficient cross-network communication remains challenging, as existing solutions like multi-hop ZigBee and one-hop LoRa entail trade-offs between communication delays and deployment costs. To tackle these issues, we propose a novel cross-interface relaying paradigm that utilizes a star topology within each ZigBee network and designates a relay node with an additional LoRa interface to bridge networks via a central LoRa gateway. Compared with existing methods, this approach reduces energy consumption and costs while improving scalability. To implement this paradigm while balancing energy conservation with delay guarantees, we introduce a Cross-network Cross-interface Relaying (CCR) scheme, which jointly schedules LoRa and ZigBee transmission behaviors to minimize energy consumption under delay constraints. CCR uses a scheduling framework that breaks down end-to-end delay constraints into link-level constraints, enabling global optimization of transmission parameters and dynamic adaptation to link quality variations. The effectiveness of CCR is demonstrated through extensive field tests on a prototype implemented on Raspberry Pi 3B+. Results show that CCR reduces energy consumption by 55.4% and 39.1% compared with an advanced LoRa communication protocol and a state-of-the-art cross-interface relaying scheme, respectively, while ensuring that 98.7% of packets satisfy their delay constraints. These findings highlight the potential of CCR for efficient and reliable cross-network communication in large-scale IoT deployments.
An effective large language model (LLM) tutor must often decline to give an answer it could easily produce. In a randomized study, students who used an unguarded chatbot scored higher while practicing but lower on a later test taken without it, whereas a Socratically guarded version of the same model kept the practice gain and removed the later loss [4]. Reliable answer-withholding is therefore central to a tutor's value, yet a capable model pressed by a frustrated student does not withhold reliably on a prompt alone. We report a deployed tutoring system that enforces answer-withholding as a per-turn, machine-checkable contract, and a method for tuning that withholding against evidence. A non-LLM policy core, reading only trusted learner state, sets a per-turn ceiling on an eight-rung help ladder; a deterministic detector strips solution code; and a separate LLM judge checks each risky reply against the contract. We tune the behavior with an automated evaluation that uses no human subjects: scripted student personas are driven through the live pipeline and re-scored by a stronger model, and we record each rejection's stated reason so failures are fixed by cause. Doing so revealed an interpretable "over-help ladder," from blatant solution leaks, to naming the exact bug, to over-citing general facts, with each fix exposing the next. The tutor reached full compliance on all four acceptance criteria. We offer the measure, diagnose, and fix loop as a reusable recipe for any LLM agent that must refuse a capability it has.
Researchers increasingly treat LLM survey responses as a proxy for human cultural values. This includes projecting model outputs onto instruments like the Inglehart-Welzel Cultural Map and drawing conclusions about which cultures a model resembles. While a model's answer to a value-laden questions may be interpreted as a cultural signal, it also carries sampling noise and, can be quite sensitive to question framing. In this paper, we separate survey responses, sampling noise and question framing for multiple LLMs. We decompose response variance from these models into variation across random seeds, prompt rewordings. We employ noise-to-signal ratio (NSR) to test whether a model's apparent cultural position is distinguishable from noise. When applied across a dozen models from four geographic origins, calibrated against 88 Integrated Values Survey countries, the answer is often no. NSR exceeds 1.0 on 49 of 117 valid model-question pairs (42
