International Radio Broadcasting and Post-Conflict State-Building: the Case of Canada’s Rana FM
Bibliographic record
Abstract
International Radio Broadcasting (IRB) has been used as a mass communication tool of the state since its inception in the early 1920s. Following its historic use in programs of propaganda, public diplomacy, psychological operations, and international development communication, the practice of IRB can also be found in a number of post-conflict statebuilding operations that are not well documented. Through a case study methodology this dissertation examines the nature of, and motivation for, the use of IRB in post-conflict state-building, as experienced by Canada’s Rana FM in contemporary Afghanistan (2006 – 2011). Using primary research from structured interviews with IRB practitioners and personal observation of IRB operations in Bosnia-Herzegovina, Kosovo and Afghanistan, this study draws on information from directing staff, management, producers, on-air presenters, and technical staff; as well as a variety of sources including internal content analyses, opinion polling, and unclassified government documents. Using the strategic communication frameworks of propaganda and development communication, this study found IRBs to function as a form of ‘defensive propaganda’ that aims to reinforce the institutions of the developing state during the process of democratic reconstruction. IRBs in post-conflict state-building can be seen to function in a surrogate capacity that aims to become a creditable source of news and information, in order to maximize audience share and provide a platform for public discussion. This dissertation presents new empirical information on Canada’s IRB in Afghanistan, Rana FM, which operated from January 2007 to July 2011.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.039 | 0.012 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".