An Assessment of Post-Taliban Media Development in Afghanistan (2002-2017)
Bibliographic record
Abstract
This research project constitutes the first of its kind in-depth baseline assessment of the growth and development of the Afghan media sector post-2001.The assessment utilizes the United Nations Media Development Indicators (MDIs) for this specific purpose.In doing so, this thesis project tests the assumption that despite pressures from both the government, the Taliban-led insurgency and other non-government actors in Afghanistan, and challenges related to financial sustainability and professionalism, the post-Taliban Afghan independent media sector is sufficiently robust, resilient and diffuse across Afghanistan and enjoys widespread political and public support that it will continue to survive and grow as a strong social and political force in the country, and thus contribute to the further entrenchment of democracy in Afghanistan, unlike all other past short-lived instances of media freedom in Afghanistan's history of struggle for constitutionalism and democracy going back to the 19 th century.I owe a profound debt of gratitude to my thesis supervisor, Prof. Christopher Dornan, for his guidance, support and great patience throughout my association with the School of Journalism -in his strong support to my initial application to the MJ program, in his teaching and throughout this research project.Prof. Dornan is a rare educator who is not only an outstanding academic but also a wise, caring teacher.I am grateful to him.I am also thankful to the many
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".