Bibliographic record
Abstract
In Canada, museums have been called to action by the country’s Truth and Reconciliation Commission (TRC) to act as sites for public education about the deep history and lasting effects of removing Indigenous children from their families and communities to be forcibly assimilated through residential schools. Museums are thus recognized as important places where Indigenous and non-Indigenous audiences alike can engage with the stories of Survivors and, by extension, engage with colonialism through tellings that counter its progressive, natural, inevitable, or celebratory framing. Across the 6 volumes of the TRC report and its 94 Calls to Action, less emphasis is placed on the potential for museum collections in the reconciliation process. Archives are positioned as sites of evidence, and art is attributed the power of reconciliation and even the precursor step of conciliation. But historic and ethnographic collections are left out of this process. In contrast, current museum anthropological practice focuses on engaging contemporary Indigenous peoples with museum collections. This chapter explores how museum anthropologists and collections can be engaged in the reconciliation process, not just of a discipline, but of a nation. I propose revisiting the relationship between history and anthropology through collaborations between history and anthropology museums. By starting with the truths both present and absent in ethnographically collected artifacts, the chapter works toward the potential of engaging with collections for reconciliation.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.018 | 0.034 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".