Human rights narratives and the right to truth
Bibliographic record
Abstract
Human rights stories are claims, told through narrative, to one or more people about something that happened that infringed or violated an agreed-upon human right. Stories can address human rights violations judicially through formal courts, commissions, or tribunals and non-judicially through public narratives in social and political discourses. Human rights stories, told as testimony in the Truth and Reconciliation Commission of Canada (2007–2015) and the #MeToo movement, demonstrate how stories about lived experiences promote human dignity and assert agency. The purpose of this analysis is to highlight the role of storytelling in both the address and redress of human rights violations within the process of telling narratives of lived experience. When states do not initiate or act through judicial processes, or those processes do not un-silence stories of violations, the emergence of informal truth-telling through public narratives emerges (Ganz, 2009). Whether told in formal judicial or informal nonjudicial spaces, sharing lived experiences is an agency asserting action that can respond to systems of oppression. Everyone ought to have a right to tell their story.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.048 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".