Bibliographic record
Abstract
The past decades public interest in history is booming. This creates new opportunities but also challenges for professional historians. This book asks how historians deal with changing public demands for history and how these affect their professional practices, values and identities. The volume offers a great variety of detailed studies of cases where historians have applied their expertise outside the academic sphere. With contributions focusing on Latin America, Africa, Asia, the Pacific and Europe the book has a broad geographical scope. Subdivided in five sections, the book starts with a critical look back on some historians who broke with mainstream academic positions by combining their professional activities with an explicit political partisanship or social engagement. The second section focusses on the challenges historians are confronted with when entering the court room or more generally exposing their expertise to legal frameworks. The third section focuses on the effects of policy driven demands as well as direct political interventions and regulations on the historical profession. A fourth section looks at the challenges and opportunities related to the rise of new digital media. Finally several authors offer their view on normative standards that may help to better respond to new demands and to define role models for publicly engaged historians. This book aims at historians and other academics interested in public uses of history.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.010 |
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".