Bibliographic record
Abstract
This research is a culmination of my experience with Madness and its presence in the world. I question what it means to experience mental health ‘trouble’ in a world that seeks to live under the guise of normalcy. Trouble, as I can most clearly navigate it, happens when something steps outside of the boundaries of normalcy. When life is experienced by an individual as something outside of social convention, the individual becomes ‘marked’ with difference based on a shared perspective of what is correct. My doctoral research therefore examines this phenomenon to understand how one lives a ‘spoiled identity’ in a world that seems to be in defiance of it. I carry out a narrative analysis of textual data to question the unquestioned and ubiquitous presence of mental health narratives within contemporary Western culture. I have not lived in poverty but I take up its narrative in connection to Madness as an intrinsic precursor to something amiss in our society. In addressing these social inequalities, I utilize key conceptual tools such as stigma/spoiled identity, narrative prosthesis, and the interplay between ‘I’ and ‘We’ narratives. As an Interpretive Sociologist, I use the lenses of ethnomethodology and phenomenology to engage in this discussion. The data collected explores and combines the individual and social experience, with Madness and poverty being the phenomena that depict this understanding. My data comes from present- day Toronto newspapers and mental healthcare programming information packages collected from four research sites in Toronto. I utilize newspapers as an object of my research because of their powerful role in narrating public and dominant views. I couple these narratives with an analysis of mental healthcare programming information packages to see where these dominant views appear within society-at-large.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".