Abstract
The Netflix series 13 Reasons Why (13RW) stirred up considerable controversy when it was first released in 2017. Although researchers across disciplines have published their work from different perspectives, results are rather mixed. The only meta-analytic review to date has identified methodological weaknesses, such as the lack of direct attribution to 13RW in aggregate studies and the biases in traditional psychological research. In this article, we contribute to the evolving scholarly investigations of 13RW by providing an alternative approach: the Galileo Model of cognitive mapping and precise measurements. We offer an overview of the Galileo theory and method, highlighting advantages of a multidimensional and non-Euclidean approach based on nearly 50 years of social scientific research. We also detail how the Galileo Model was used to study 13RW, by framing Season 1 as “compound suicide messages.” We found that self-concept was very close to 13RW main character HANNAH and SUICIDE among highly suicide-prone individuals who posted on Reddit online forums both related to 13RW and suicide watch. However, when we used the Galileo software suite and a ratio-scaled instrument with college freshmen in a quasi-experiment, we found no significant difference between viewers and non-viewers overall, although female viewers perceived themselves to be closer to SUICIDE than male viewers. The Galileo Model demonstrated its sophistication to capture and detect the nuances in understanding the complexity of suicide for different population groups and showed tremendous potential for more informative media effects evaluation and formative research to tailor compound messages with social consciousness before they reach the mass audiences.
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We thank Alex Moskowitz and Jonathan Jones for their assistance with data entry, and Juliet Parris for her comments on the manuscript.
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Wang, H., Woelfel, J. Netflix series 13 reasons why as compound suicide messages: using the Galileo model for cognitive mapping and precise measurements. Qual Quant 56, 751–768 (2022). https://doi.org/10.1007/s11135-021-01145-3
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DOI: https://doi.org/10.1007/s11135-021-01145-3