Bibliographic record
Abstract
As keepers of “difficult heritage,” human rights museums are responsible for navigating the needs of diverse groups, often with competing interests and missions. It is commonly argued that the primary role of these museums is to provide human rights education; however, survivor and their descendant communities increasingly demand that museums advocate for and be accomplices to community struggles. To understand why acting reciprocally with survivors and their descendants is a crucial responsibility of the human rights museum, this paper considers two examples: Greenwood Rising in Tulsa, Oklahoma and the War and Women’s Human Rights Museum in Seoul, South Korea. Subsequent discussion of these case studies assesses how collaborating with survivor and descendant communities, involving governing powers, and maintaining visitor empathy affect a human rights museum’s ability to minimize harm, share authority, and respond to community needs. Ultimately, this paper argues that human rights museums must be activists and care for communities to avoid exploiting human suffering.
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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.134 | 0.037 |
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".