Critical Ancient World Studies: The Case for Forgetting Classics
Bibliographic record
Abstract
This volume explores and elucidates critical ancient world studies (CAWS), a new model for the study of the ancient world operating critically, setting itself against a long history of a discipline formulated to naturalise a hierarchical, white supremacist origin story for an imagined modern West. CAWS is a methodology for the study of antiquity that shifts away from the assumptions and approaches of the discipline known as classical studies and/or classics. Although it seeks to reckon with the discipline’s colonial history, it is not simply the application of decolonial theory or the search to uncover subaltern narratives in a subject that has special relevance to the privileged and powerful. Rather, it dismantles the structures of knowledge that have led to this privileging, and questions the categories, ideas, themes, narratives, and epistemological structures that have been deemed objective and essential within the inherited discipline of classics. The contributions in this book, by an international group of researchers, offer a variety of situated, embodied perspectives on the question of how to imagine a more critical discipline, rather than a unified single view. The volume is divided into four parts – “Critical Epistemologies”, “Critical Philologies”, “Critical Time and Critical Space”, and “Critical Approaches” – and uses these as spaces to propose disciplinary transformation. Critical Ancient World Studies: The Case for Forgetting Classics is a must-read for scholars and practitioners teaching in the field of classical studies, and the breadth of examples also makes it an invaluable resource for anyone working on the ancient world, or on confronting Eurocentrism, within other disciplines. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution (CC-BY) 4.0 license.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.018 | 0.070 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".