Rebuilding epistemic authority: strategic transparency and digital ethnography in policing and intelligence
Bibliographic record
Abstract
In the post-truth era, policing and intelligence agencies face a dual crisis: navigating complex security threats while confronting a collapse in public trust and epistemic authority. When truth itself becomes contested, how can institutions tasked with safeguarding society maintain legitimacy and authority? This commentary argues that restoring legitimacy requires more than technological fixes or piecemeal procedural reforms. It demands a structural transformation in how institutions communicate, engage and sustain truth within democratic societies, where trust and truth are deeply co-dependent. Drawing on examples from the United States, Canada, Australia and the UK, the article introduces strategic transparency as a principled framework for balancing operational secrecy with public accountability. Furthermore, by developing new competencies such as digital ethnography, agencies can better understand and counter disinformation while reinforcing public trust. In an era where facts are contested and trust is fragile, strategic transparency offers a paradigm shift for rebuilding epistemic resilience and democratic legitimacy – ensuring that institutions not only defend national security but also serve as credible stewards of truth in a contested information environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".