How was the Taliban 2.0 in Afghanistan seen in Pakistan?
Bibliographic record
Abstract
The Taliban 2.0 in Afghanistan took the world by surprise. This article investigates how this event was seen differently in varied contexts, such as neighboring Pakistan. Our research shows epistemological pluralism in Pakistan, i.e. how different groups use different ways of knowing (epistemology), being (ontology), and valuing (axiology) to explain and analyze Taliban 2.0. Conceptually, the paper draws on insights from the relationality theory to demonstrate the reasons behind such epistemological pluralism. The theory of relationality provides the grounds for epistemological pluralism, i.e. the mixed sentiments and feelings among respondents about the Taliban 2.0 in Afghanistan. Our research reports the perspectives of nine selected civil society activists about Taliban 2.0 in Afghanistan and its implications for Pakistan. The respondents were interviewed during the second quarter of the Taliban administering Afghanistan. Some called it the victory of Islam, the freedom of Afghans from foreign occupation, and the protection of the Pakistani border from Indian proxies. Others were worried about the risks of increased extremism and terrorism in Pakistan, including the rise of banned organizations like Tehrik-e-Taliban Pakistan. This study intends to document the interviewee civil society activists’ suggestions to the State of Pakistan for dealing with Taliban-ruled Afghanistan.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".