Activating Personal Counter-Archives: The Case of the Amir Hassanpour Fonds
Bibliographic record
Abstract
ABSTRACT In this article, through a historical and theoretical reflection on the Amir Hassanpour Fonds, held at the University of Toronto, the author investigates the role of personal and community archives in counter-archiving in the Middle East, especially in Iran. He expands on his experience working on appraising, describing, and arranging the Amir Hassanpour Fonds at the University of Toronto Archives and Records Management Services (UTARMS) to argue that archiving a diasporic and revolutionary counter-archive calls for more than a procedural and habitual institutional practice. To activate such a counter-archive, there should be, first, close attention paid to the memory institutions against which the counter-archive has been developed. Such close attention calls for an investigation into the larger political structures in which the memory institutions are embedded. Second, the counter-archive should be understood in the broader modes and methods of cultural and memory resistance in that specific political structure, engaging analytically and theoretically with the creator's counter-archiving praxis, in this case Amir Hassanpour's Marxist internationalist revolutionary approach. The article ends with a brief discussion of the various ways UTARMS attempted to enliven this specific fonds.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.042 | 0.031 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".