Attitudes towards incels - A meta study on negatives attitudes in research
Bibliographic record
Abstract
This thesis addresses incels or involuntary celibates. A group that entered the sight of mainstream media in 2018 after the Toronto van attacks, and since have garnered an increasing amount of media attention. Several mass-killers have been linked to the group, either self-identifying or describing the same plights that incels describe. However, with the increase of focus on incels in mainstream media there has also been an increase on academic articles on incels. In this thesis I seek out to understand the academic work that has been published on incels and whether these articles are written with a sufficient understanding of internet culture and if they have a sufficient understanding of incels. Conducting a meta-study on existing literature on incels chosen by conducting a rapid review with set criteria through search engines such as Google Scholar and Oria I have found articles that suit this thesis well. To analyse the data I have chosen to go with Toulmin´s model for argument analysis to break down the arguments made in the existing literature on incels to find whether there are pre-conceived attitudes from the authors found within the data material. This thesis seeks to broaden the understanding of incels through a neutral understanding of the group.
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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 | 0.093 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".