Bibliographic record
Abstract
Abstract Decolonization is the subject of an abundant literature, both as a historical event and as a contemporary process. In relations with the past, debates have risen about issues such as colonial monuments, museum collections, and repatriation. Rather than dealing with a specific type of space, institution, or material, this special issue in International Public History offers a discussion on the many links between decolonization and public history. The articles explore if and to what extent public history practices can contribute to decolonizing the history production process (through decolonized sources, decolonized interpretation processes, and decolonized space of communication of history). The articles discuss what ‘public’ in ‘public history’ means: who is doing history, for whom, with whom, and for what? The self-reflective approach of public history also questions the colonial bias and processes at stake in institutions such as archives, museums, and universities. The special issue includes contributions from various countries (South Africa, Kenya, Brazil, Canada, and Japan) to foster discussions on the plurality of links between public history and decolonization in an international context that goes beyond the too-often Western oriented public history frameworks.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".