Krista McCracken and Skylee-Storm Hogan-Stacey, Decolonial Archival Futures
Bibliographic record
Abstract
career, and an alternative -not the centre -of the historical profession.The supposed discovery of the discipline only during these periodic gnashing of teeth over the future of graduate history programs infuriates public historians.These discussions also imply that anyone with a history PhD can be a public historian with just a few tweaks.Collectively, the authors of this volume firmly disprove this latter notion.Brock and Faulkenbury set three audiences for this volume: public historians at universities; other instructors who teach with similar pedagogical practices; and university administrators.Public history instructors will be inspired by the projects and lessons learned, and find solace in the experiences of their colleagues.University public historians are most often programs of one which can be isolating.Professors in non-history departments who also lead collaborative, community-based, and experiential learning courses can also find useful teaching strategies.Administrators pondering a public history course or program should also read this book, but as a caution.Public historians welcome attention towards their discipline at universities, but few administrators who see it as a tactic to improve student recruitment or solve history teaching jobs crises understand the workload implications for professors, the financial investment to support collaborative community projects or student internships, or the ethical issues in (mis)representing history PhDs as sufficient training to enter the public history workforce.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".