Authentic Representation and Author Identity: Exploring Mental Illness in The Hobbit Fanfiction
Bibliographic record
Abstract
This paper addresses concerns with authenticity claims that surround mental illness and author identity in fanfiction. I will apply the critiques surrounding representation in media (see Mitchell and Snyder, 2001; Couser 2003, 2009) found in disability studies and fandom studies (see Jenkins 2012) to fanfiction. In this paper, I analyze two pieces of fanfiction which focus on Thorin II also known as Thorin Oakenshield, a character from J.R.R Tolkien’s The Hobbit novel and Peter Jackson’s film adaptations. I explore how in these texts the authors portray mental illness through their characterization of Thorin II. How the author’s actual or perceived personal mental health status may impact their writing, and readers’ responses to their writing, is explored through the lenses of identity politics (see Calhoun, 1994) and authenticity (Couser, 2009; van Dijk, 1989). In the context of disability and fandom studies, these fanfictions act as examples of a) combination fictional/ autobiographical writings which work to provide what the authors’ perceive as accurate portrayals of mental illness, and b) how the author’s mental health status impacts the perceived credibility of their work.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.015 | 0.039 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".