Bibliographic record
Abstract
This article explains the value of assemblage theory to making sense of a museum like the Canadian Museum for Human Rights (CMHR), which has struggled with the formidable challenge of comparatively representing human rights in controversial cultural and historical contexts. I argue that “assemblage thinking” permits us to appreciate more richly the way in which the expressive power of the CMHR arises from the dynamic interaction/intersection of overlapping clusters of objects, spaces, ideologies, memories, feelings, structures, histories, and experiences. Understood as “assemblages,” these clusters in important (but not all) ways lie beyond the scope of formal agency such as that exercised by curators and museum administrators. Accordingly, we must understand museums generally, and the CMHR particularly, as fundamentally unable guarantee the integrity and perdurability of their/its own structures and meanings, and recognize these meanings (and a museum’s identity) as irreducibly open-ended and provisional.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.026 | 0.052 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".