Detoxifying Masculinity through Social Media : A Netnographic Case Study of Professor Neil’s Instagram Channel and Affective Publics
Bibliographic record
Abstract
This paper, "Detoxifying Masculinity through Social Media: A Netnographic Case Study of Professor Neil’s Instagram Channel and Affective Publics," explores the changing frameworks of masculinity on social media, by looking at the Instagram channel and comment section of @professor_neil’s Instagram. This thesis was inspired by the emergence of male content creators on TikTok and Instagram that counter the messaging of the manosphere. This study integrates Gramsci’s theory of hegemony, along with Connell's framework of hegemonic masculinity and Anderson’s Inclusive masculinity theory. Furthermore, I utilize Papacharissi’s application of affect theory on networked publics. The study explores the content on Professor Neil Shyminsky’s (@professor_neil’s) Instagram channel a Professor of English in Canada that makes content which critiques manosphere content from a feminist and LGBTQ+ perspective. My netnographic study combines passive observation, field notes, archival data and Critical Discourse Analysis of @professor_neil’s reels and comment section. These methodologies aimed to define and discover what kind of masculinity inhabits his channel and which types of masculinity other users affectively connect to and promote. The data collected show strong evidence that a new type of inclusive masculinity is present on social media in conjunction and contest with conservative or more ‘traditional’ masculinity. My thesis aims to contribute to the ongoing understanding of how social media can positively impact how masculinity is defined and experienced and how sociopositive masculinities impact social spaces. Furthermore, this thesis explores how affective publics can connect with and promote new and healthier approaches to gender. As a case study, my work aims to facilitate future research on merging masculinities in social media, and the world in which they exist.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.029 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".