Bibliographic record
Abstract
I’m Lauren, I’m 28 years old. I live just outside of Toronto, Ontario and I volunteer with an organization called life after hate, which works to help individuals disengage from hate groups. I spent 5 years in hate groups and remember how difficult it is to leave that toxic network behind. I also hope to make amends for the damage I once did. It’s just like an addiction; I was attracted to it because I was looking for somewhere I could escape to where I felt significant in my own mind. I was raised in an middle class family and from the outside; we looked like we had everything. Since I was a kid, something just internally felt really off, like I wasn't comfortable in my own skin. My dad was sick with cancer since I was 7 years old. My grandfather was toxic and would regularly make nasty comments about my weight, my appearance, my academic short comings at school and anything else he could find. I didn't know what to do about my grandfathers actions; I tried to loose weight, but it seemed no matter what I did, it was never good enough for him. My dad was my best friend and died when I was 16, there after I was left with this huge void.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 0.017 |
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".