No more sources? The impact of Snowden's revelations on journalists and their confidential sources
Bibliographic record
Abstract
From June 2013 documents leaked by the National Security Agency (NSA) dissident Edward Snowden revealed that Western intelligence agencies are capable of bulk collection of electronic communications flowing through global telecommunication systems. Surveillance data shared by the ‘Five Eyes’ eavesdropping agencies of the US, UK, Canada, Australia and New Zealand include journalist’s communications. In the wake of Snowden leak, Zygmunt Baumann et al called for a systematic assessment of the scale, reach and character of contemporary surveillance practices (2014, 122). This paper explores a specific part of Bauman’s task by assessing the impact of the Snowden revelations on confidential source-based journalism. Interviews were conducted with a range of investigative journalists who have experience of covering national security in Five Eyes countries. All expressed serious concern over the intelligence agencies’ greatly enhanced capability to track journalists and identify and neutralise their sources. The paper concludes that there is clear evidence of a paradigmatic shift in journalist-source relations as those interviewed regard Five Eyes mass surveillance as a most serious threat to the fourth estate model of journalism as practiced in Western democratic countries.
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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 | 0.061 | 0.227 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".