Choice of handshape and classifier type in placement verbs in American Sign Language

Abstract This study investigates choice of classifier handshape in the placement verbs MOVE and PUT in American Sign Language (ASL), analyzing data from two deaf ASL signers describing placement events elicited via a semi-structured task. Two questions are addressed in the study: 1) which (handling) handshapes are used with PUT and MOVE for different types of entities, and 2) whether object classifiers can occur with transitive, causative predicates in ASL. The results show that the two signers primarily use three handling handshapes (flat-O, S, and C). Consistent with findings from previous research, these handshapes are generally used to represent entities in an iconic manner based on entity characteristics. Usage of the flat-O handshape extends beyond strictly iconic representation, confirming previous suggestions that it may serve as a generic classifier for handling objects. The study also reports a finding not previously observed for ASL, namely two instances of object classifiers used in transitive predicates, which parallels recent evidence from other sign languages that the relationship between classifier type and argument structure is not categorical. Assimilation and language-specific handshape bias are discussed as factors influencing classifier choice, and the findings are situated within broader principles of body partitioning, perspective, and type of representation in sign languages.

Authors

Institutions

Publication Details

Journal
Open Linguistics
Published
2025-01-01
DOI
https://doi.org/10.1515/opli-2025-0042
Primary Topic
Hearing Impairment and Communication
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Choice of handshape and classifier type in placement verbs in American Sign Language

Anne Therese Frederiksen
Open Linguistics
Hearing Impairment and Communication
article

Choice of handshape and classifier type in placement verbs in American Sign Language

Anne Therese Frederiksen
article en

Abstract

Abstract This study investigates choice of classifier handshape in the placement verbs MOVE and PUT in American Sign Language (ASL), analyzing data from two deaf ASL signers describing placement events elicited via a semi-structured task. Two questions are addressed in the study: 1) which (handling) handshapes are used with PUT and MOVE for different types of entities, and 2) whether object classifiers can occur with transitive, causative predicates in ASL. The results show that the two signers primarily use three handling handshapes (flat-O, S, and C). Consistent with findings from previous research, these handshapes are generally used to represent entities in an iconic manner based on entity characteristics. Usage of the flat-O handshape extends beyond strictly iconic representation, confirming previous suggestions that it may serve as a generic classifier for handling objects. The study also reports a finding not previously observed for ASL, namely two instances of object classifiers used in transitive predicates, which parallels recent evidence from other sign languages that the relationship between classifier type and argument structure is not categorical. Assimilation and language-specific handshape bias are discussed as factors influencing classifier choice, and the findings are situated within broader principles of body partitioning, perspective, and type of representation in sign languages.

Open LinguisticsVol. 11(1)
City University of New York (US), Brooklyn College (US)
Tow Foundation
Openalex Percentile: Top 94%
Hearing Impairment and Communication
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.