Applied Linguistics: Academic Discipline — Prolific Citations Library

Explore 2 peer-reviewed papers in Applied Linguistics (2025). Academic research using Prolific for high-quality human data collection.

This page lists 2 peer-reviewed papers in the discipline of Applied Linguistics in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (2 of 2)

Crowdsourced comparative judgement for evaluating learner texts: How reliable are judges recruited from an online crowdsourcing platform?

Authors: P Thwaites, N Vandeweerd, M Paquot

Year: 2025

Published in: Applied Linguistics, 2025 - academic.oup.com

Institution: University College Londonouvain, Radboud University Nijmegen, Fonds de la Recherche Scientifique – FNRS

Research Area: Applied Linguistics, Educational Assessment, Crowdsourcing

Discipline: Applied Linguistics

The study demonstrates that crowdsourcing platforms can recruit judges to evaluate learner texts with reliability and validity comparable to assessments conducted by trained linguists.

Methods: Judges recruited via an online crowdsourcing platform conducted comparative judgement assessments of learner texts to measure writing proficiency.

Key Findings: Reliability and concurrent validity of learner text evaluations performed via crowdsourced judges compared to linguist evaluations.

Citations: 10
Beyond Face Value: Visual and Auditory Signals in Human and Machine Trust Judgments

Authors: N Tyulina, Y Yu, TA Emmanouil, SI Levitan

Year: 2025

Published in: Proceedings of the 7th ACM ..., 2025 - dl.acm.org

Institution: University of Cambridge, University of Bath, University of Edinburgh, New York University

Research Area: Human-AI Interaction, Trust and Perception, Nonverbal Communication

Discipline: Applied Linguistics

Trust judgments are primarily influenced by auditory cues in both humans and multimodal models, though subtle differences in modality weighting exist between them.

Methods: Behavioral experiment with trust ratings of bimodal stimuli across four trust congruence conditions, combined with a multimodal model trained using HuBERT and ResNet-50 with late fusion, analyzed using Permutation Feature Importance (PFI).

Key Findings: The construction of trust from visual and auditory signals in both humans and multimodal models, focusing on modality dominance and feature weighting.

Sample Size: 150