Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the importance of classification structures to efforts at holding perpetrators of human rights abuses accountable using one archival repository in Cambodia as a case study. Design/methodology/approach The primary methodology of this paper is a textual analysis of the Documentation Center of Cambodia's classification scheme, as well as a conceptual analysis using the theoretical framework originally posited by Bowker and Star and further developed by Harris and Duff. These analyses were supplemented by interviews with key participants. Findings The Documentation Center of Cambodia's classification of Khmer Rouge records by ethnic identity has had a major impact on charging former officials of the regime with genocide in the ongoing human rights tribunal. Social implications As this exploration of the DC‐Cam database shows, archival description can be used as a tool to promote accountability in societies coming to terms with difficult histories. Originality/value This paper expands and revises Harris and Duff's definition of liberatory description to include Spivak's concept of strategic essentialism, arguing that archivists’ classification choices have important ethical and legal consequences.
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 | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".