https://ojs.library.ubc.ca/index.php/storyboards <p><em>Semantic Fieldwork Methods </em>is dedicated to the discussion of innovative techniques and materials for use in semantic and pragmatic fieldwork. We invite contributions which explain and illustrate how hypotheses about meaning can be tested in a fieldwork setting. </p> University of British Columbia, Department of Linguistics, Vancouver, BC, Canada en-US Semantic Fieldwork Methods 2562-9271 <p>Authors of articles retain the copyright of the text and data in the article itself, unless otherwise specified in the article.</p><p>However, storyboards and other visual materials that accompany the articles are distributed with the Creative Commons Attribution 2.5 Canada (CC-BY 2.5 CA) license, which allows the creation of derivative works (including commercial derivative works). To redistribute a storyboard or other visual material in any form, modified or unmodified, y<span>ou must give <a id="appropriate_credit_popup" class="helpLink" title="" href="https://creativecommons.org/licenses/by/2.5/ca/" data-original-title="">a</a>ppropriate credit to the original author</span><span>, provide a link to the license, and indicate if changes were made</span><span>. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. It is not necessary to license a derivative work with the CC-BY CA 2.5 license or any other Creative Commons license.</span></p> https://ojs.library.ubc.ca/index.php/storyboards/article/view/197041 <p class="p1">No single test is robust or exhaustive enough to accurately establish the meaning of tense, mood and aspect (TMA) markers. This article explores the benefits of using canonical and statistical approaches and a variety of elicitation tasks. The testing ground for this multi-faceted approach is past marking in Mauritian Creole. Alongside common methods such as translation and acceptability judgements, data collection also included cloze tests with meta-discussion, narrative re-telling and interviews. Since each task has specific shortcomings, the data is best understood altogether, made possible due to analyzing all tasks within a common framework. The results show that two main past markers (TI and FINN) are generally in complementary distribution, and less common markers (FEK and Ø) broadly pattern with one of these, constituting a novel finding. Adopting this methodology allows for a finer-grained understanding of TMA marking and enables researchers to counteract specific biases associated with individual tasks.</p> Hannah Davidson Copyright (c) 2024 Hannah Davidson https://creativecommons.org/licenses/by/2.5/ca/ 2024-11-13 2024-11-13 6 4 1 39 10.14288/sfm.v6i4.197041