- Award ID(s):
- 1725520
- NSF-PAR ID:
- 10290158
- Date Published:
- Journal Name:
- Journal of Chemical Education
- Volume:
- 98
- Issue:
- 8
- ISSN:
- 0021-9584
- Page Range / eLocation ID:
- 2490 to 2495
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
null (Ed.)Findings from embodied cognition suggest that our whole body (not just our eyes) plays an important role in how we make sense of data when we interact with data visualizations. In this paper, we present the results of a study that explores how different designs of the ”interaction” (with a data visualization) alter the way in which people report and discuss correlation and causation in data. We conducted a lab study with two experimental conditions: Full body (participants interacted with a 65” display showing geo-referenced data using gestures and body movements); and, Gamepad (people used a joypad to control the system). Participants tended to agree less with statements that portray correlation and causation in data after using the Gamepad system. Additionally, discourse analysis based on Conceptual Metaphor Theory revealed that users made fewer remarks based on FORCE schemata in Gamepad than in Full-Body.more » « less
-
more » « less
Social media users have long been aware of opaque content moderation systems and how they shape platform environments. On TikTok, creators increasingly utilize algospeak to circumvent unjust content restriction, meaning, they change or invent words to prevent TikTok’s content moderation algorithm from banning their video (e.g., “le$bean” for “lesbian”). We interviewed 19 TikTok creators about their motivations and practices of using algospeak in relation to their experience with TikTok’s content moderation. Participants largely anticipated how TikTok’s algorithm would read their videos, and used algospeak to evade unjustified content moderation while simultaneously ensuring target audiences can still find their videos. We identify non-contextuality, randomness, inaccuracy, and bias against marginalized communities as major issues regarding freedom of expression, equality of subjects, and support for communities of interest. Using algospeak, we argue for a need to improve contextually informed content moderation to valorize marginalized and tabooed audiovisual content on social media.
-
Microplastic pollution is measured with a variety of sampling methods. Field experiments indicate that commonly used sampling methods, including net, pump, and grab samples, do not always result in equivalent measured concentration. We investigate the comparability of these methods through a meta-analysis of 121 surface water microplastic studies. We find systematic relationships between measured concentration and sampled volume, method of collection, mesh size used for filtration, and waterbody sampled. Most significantly, a strong log−linear relationship exists between sample volume and measured concentration, with small-volume grab samples measuring up to 104 particles/L higher concentrations than larger volume net samples, even when sampled concurrently. Potential biasing factors explored included filtration size (±102 particles/L), net volume overestimation (±101 particles/L), fiber loss through net mesh (unknown magnitude), intersample variability (±101 particles/L), and contamination, the potential factor with an effect large enough (±103 particles/L) to explain the observed differences. On the basis of these results, we caution against comparing concentrations across multiple studies or combining multiple study results to identify regional patterns. Additionally, we emphasize the importance of contamination reduction and quantification strategies, namely that blank samples from all stages of field sampling be collected and reported as a matter of course for all studies.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1021/acs.jchemed.1c00532
