Bibliographic record
Abstract
In late October 2014, accusations of sexual assault levelled against Jian Ghomeshi dominated the Canadian news cycle. This case offers an opportunity to examine the public’s struggle to determine whether to believe Ghomeshi’s alleged victims, to make sense of how that belief matters, and ask what responses these beliefs demand. This project is a narrative study of expressions of belief in the Ghomeshi scandal. In this project, I use a multi-step approach to explore what it meant for participants to say they believed or did not believe Ghomeshi or his alleged victims. In order to first characterize the context in which those comments were made, in Chapter 1 I sketch a broad timeline of events that make up the Ghomeshi scandal using news articles and publicly available online media. In Chapter 2, I detail a discursive analysis of Ghomeshi’s Facebook post which publicly triggered the scandal. In Chapter 3, I provide a thematic analysis of the responses to the Facebook post made by commenters on the same platform. Finally, in Chapter 4 I present a discursive psychological analysis of what the expressions of belief in these responses might mean, an analysis I augmented with a discussion of Charles Taylor’s strong evaluations. I conclude with a discussion of this work.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.025 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".