Bibliographic record
Abstract
There is a tradition and practice in libraries and other Western and information institutions of collecting, stewarding, and taking “cultural heritage” materials from non-Western communities or about non-Western subjects. Attention to the practice of collecting cultural heritage is heightened during times of perceived threat, vulnerability, or destruction of these materials, and the ways in which libraries and memory workers can intervene in their protection. Framed as removal and rescue, the practice and narrative around absorbing “at risk” heritage materials reveals a set of assumptions about how libraries and information institutions attempt to decontextualize themselves from the world; this essay will unpack this framing, its outcomes, whose interests it serves, and the kinds of politics it extends and legitimizes. Utilizing textual analysis of mainstream library guidelines and communications, and drawing on principles of critical librarianship, art criticism, and anti-colonial writing, which point to the embeddedness of library and information work within regimes of power, this chapter will problematize the tendency of our fields to deploy rhetorics of inclusion—and the politics that underpin that rhetoric—in its justification of heritage absorption and representation, namely, that it inherently contributes to a social justice project, is universally desired, and serves an imagined “public good.”
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.070 | 0.015 |
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".