Open Secrets, Off the Record: Audience, Intimate Knowledge, and the Crisis of the Post-Apartheid State
Bibliographic record
Abstract
This article reflects on the ways that individuals, communities, and political organizations sometimes invest in an understanding of history based on their lived experiences or, to borrow a term from Hugh Raffles, their “intimate knowledge.“ Debates regarding the practice of public history often begin with the question of how historians can—or should—address the abstract figure of “the public.“ In contrast, this article discusses how individuals and communities with political, emotional, and ideological commitments to a particular historical narrative emerge as an audience through their decision to engage, or sometimes not to engage, with the historian's research and publications. Drawing on my own research into the life of a South African anti-apartheid activist, Dr. Abu Baker “Hurley“ Asvat, this article also analyzes how contemporary struggles for historical visibility not only shape the terrain and process of academic research, but can also draw the historian's practice of writing into a broader landscape of discursive and political battles over the past.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.054 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".