Resisted Access? National Security, the Access to Information Act , and Queer(ing) Archives
Bibliographic record
Abstract
The anti-homosexual security purges organized by the Security Panel and enforced by the RCMP, represent a sad chapter in Canadian Cold War history. This essay offers some of the author’s experiences as she negotiated the “maze” of classified documents, archives, and historical records held at government departments while researching her book The Canadian War on Queers: National Security as Sexual Regulation, co-authored with Gary Kinsman. The author contends that the national security state can deploy the Access to Information Act (ATI) to create challenges and obstacles for queer historians in their effort to find queers in – or to queer – the archives. The essay also speculates that in the context of the “war on terror,” the AntiTerrorism Act (ATA) will reinforce the negative impact of ATI and thus have detrimental implications for the writing of queer history.RÉSUMÉ Les purges de sécurité anti-homosexuelles organisées par le Security Panel et imposées par la GRC, constituent un triste chapitre dans l’histoire canadienne de la Guerre froide. Ce texte raconte certaines expériences de l’auteure alors qu’elle a traversé un « labyrinthe » de documents classifiés, de documents d’archives et de documents historiques conservés dans des ministères pendant qu’elle faisait de la recherche pour le livre The Canadian War on Queers: National Security as Sexual Regulation, qu’elle a écrit avec Gary Kinsman. L’auteure soutient que l’État de sécurité nationale peut se servir de la Loi sur l’accès à l’information (AI) pour créer des défis et des obstacles pour les historiens des minorités sexuelles qui sont à la recherche de contenu gai et lesbien dans les archives. Le texte avance aussi l’hypothèse que dans le contexte de la « Guerre contre la terreur », la Loi antiterroriste renforcera l’impact négatif de l’AI, ce qui aura des répercussions nuisibles pour la rédaction de l’histoire gaie et lesbienne.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".