ÇİN-PAKİSTAN EKONOMİ KORİDORU VE PAKİSTAN TWITTER UZAMINDA KANAAT TEKNİSYENLERİ: BİR TEMATİK İÇERİK ANALİZİ
Bibliographic record
Abstract
The China-Pakistan Economic Corridor (CPEC) is the most crucial trade route in contemporary South Asia which connects Pakistan's Gwadar port located in Balochistan province and China's Kashgar, shortening the Middle Eastern oil route for China.It happens to be a core project of China's Belt and Road Initiative (BRI) which embodies Chinese alternative globalization and encompasses commercial and cultural routes and infrastructure in the participating countries.Although the BRI mainly involves the state institutions of the participating nations, the historic nature of cultural, political and economic relationships of these countries with China affect the political engagement and shape the public discussion about the BRI and its regional projects including the CPEC.Just like each participating country, Pakistan also attaches discrete significance to BRI and puts extraordinary emphasis to secure its respective regional and economic interests, while China has also boosted its public and cultural diplomacy to make ground for its successful execution.This study has undertaken a thematic analysis of the contents produced by 'opinion technicians' on Twitter from Pakistan during and immediately after China's Second Belt Road Forum (2019) as Pierre Bourdieu asserts that officials, opinion leaders, and leading institutions qualify to become the opinion technicians and shape dominant public opinion by the application of framing and priming in the light of local politics and agendas.The study found that the technicians of opinion are effectively adopting the multi-thematic discourse, and portray the CPEC as landmark project which has already started economic and industrial transformation in Pakistan and also holds potential benefits such as poverty alleviation, foreign investments and extended access to Chinese markets to exemplify the win-win cooperation in near future.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".