"I Work in Social": Community Managers and Personal Branding in Social Media
Bibliographic record
Abstract
Welcome to a world of networking, hustling, coffee, cupcakes, and cocktails; a world where social media is not only an interest, but a way that people meet, become friends, make money, and stay connected. Here, social media is both a passion and a profession, but there is also an unspoken tension. My research analyzes how social media managers—those who manage online communities and create content across digital platforms—work in social media (referring to the work practice of social media managers), and also do the work of social media (referring to the curation of their personal brand using social media to leverage a strategic advantage in the job market). Using a mixed method approach, including three years of fieldwork in Toronto and semi-structured interviews with social media professionals, this dissertation analyzes the “social media scene” and identifies elements of the changing landscape of work and self-presentation in an age of social media. The research examines the practice of community management across various industries, including marketing agencies, entertainment, not-for-profit, education, government, telecommunications, retail, and sports. The dissertation uncovers an emerging feminization of social media within the profession. I argue that social media management represents the next iteration of the devaluation of women’s work in the tech industry, mirroring the history of women’s labour in technology. I uncover how people are adopting personal branding practices on social media as a strategy to gain control of their own lives and careers against conditions of increased corporatization, job insecurity, and precarious economic times. I explore the influencer economy and introduce the term “casual influencer” to refer to an ordinary person who posts sponsored content on behalf of a brand, and are typically compensated with free “swag” or experiences, which points to the commercialization of community. The dissertation contributes to our understanding of the inherent contradictions and binaries of living and working in a digitally mediated world: the double labour of working on personal branding as an integral aspect of being an employee; the dissolving divide between the personal and the professional; and the visibility of influence and invisibility of disclosure inherent in social media.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".