In the name of terror?: Information and policy in the decade post 9/11
Bibliographic record
Abstract
Abstract It seems that every passing week reveals new developments in the ways in which information policies are being implemented throughout society. Tragedies like the Boston Marathon bombings of March 2013, along with the VIA Rail train plots in the Toronto‐Montreal corridor remind and compel us to examine the decade that has passed since the events of 9/11 and the subsequent legislative and policy impacts on information. The aim is to provide information professionals and scholars with an open forum to critically reflect on post‐9/11 legislation, policies, and practices and how they impact access to information and informational activities more generally, including the production, management, and diffusion of public information. With an eye toward the future, we examine the extent to which the discourses and practices of the past decade have contributed to shaping and reshaping our information environment, how the information field has responded in the ten years since this defining event, and why and how information professionals ought to remain engaged in these matters in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.009 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".