Accessing Information in a Nascent Technology Industry: Tracing Canadian Drone Stakeholders and Negotiating Access
Bibliographic record
Abstract
This chapter discusses the challenges of access to information and privacy (ATIP) requests as part of a multi-pronged methodology in investigating the proliferation of unmanned aerial systems (UAS) in Canada The role of ATIPs has been pivotal in investigating the policy-making for, and proliferation of, unmanned aerial systems (UAS) in Canada. However, while ATIP requests are a useful methodological strategy to trace transformations within a particular space, they “rarely lead to full-picture explanations” of the topic at hand (Monaghan and Walby 2012, 138), and must often be supplemented with additional methods. Initial findings from ATIP data highlighted a revolving door of stakeholders in Canada’s nascent drone industry with key stakeholders moving back and forth between roles in government, industry, and the military (see Bracken-Roche 2016, Gersher 2013, Hayes 2012). ATIPs and the other research methods deployed often encountered the same spins, stalls, and shutdowns seen across security and surveillance research (Lippert et al., 2016). In a small, emerging industry ATIP requests can be quite political and might hinder other methods of access, and thus must be balanced against how they help or hinder access (interviews, in this case). This resulted in the need to balance formal access avenues such as ATIPs versus gathering data directly from stakeholders as a result of correspondence and rapport building prior to, during, and after conferences and interviews.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.031 | 0.009 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".