The Productivity Paradox in Turkish Agriculture: Can AI-Supported Thematic Analysis Provide a Solution?/ Türk Tarımında Verimlilik Paradoksu: Yapay Zekâ Destekli Tematik Analiz Bir Çözüm Sağlayabilir mi?

Öz

Bu çalışma, nitel araştırma yöntemleri arasında tematik analizin en önemli sınırlamalarından biri olan zaman maliyeti sorununu ele almakta ve bu sorunu çözmek için AI destekli bir hibrit model önermektedir. Araştırmanın odak noktası, Türk tarım sektöründeki “verimlilik paradoksu”dur (bol kaynaklara rağmen düşük verimlilik). Çalışmada, Braun ve Clarke'ın (2006) geleneksel altı adımlı tematik analiz protokolü ve Keshav'ın (2007) üç aşamalı sistematik okuma modeli, metodolojik bir çerçeve geliştirmek için yapay zeka ile entegre edilmiştir. Web of Science (WoS) veritabanından “Türkiye” ve ‘tarım’ anahtar kelimeleri kullanılarak toplanan toplam 985 akademik makale, iki farklı büyük dil modeli (LLM) kullanılarak analiz edilmiştir: ChatGPT ve DeepSeek. İlk aşamada, her iki model tarafından “yüksek alaka düzeyi” olarak işaretlenen 268 makale kesişim kümesi olarak belirlenmiş ve bu veri kümesi üzerinde derinlemesine analiz yapılmıştır. İkinci ve üçüncü aşamalarda, kod önerisi, tematik kümeleme ve tutarlılık analizi gibi yapay zeka yetenekleri kullanıldı; ancak nihai yorumlama, sentez ve anlamlandırma süreçleri insan araştırmacılar tarafından gerçekleştirildi. Bulgular, Türk tarımındaki verimlilik paradoksunun, doğal kaynak yönetimi, teknolojik adaptasyon, iklim değişikliği, politika yönetişimi, sosyoekonomik dinamikler ve su yönetimi gibi birbiriyle bağlantılı çok boyutlu temalardan kaynaklandığını ortaya koydu. Önerilen hibrit modelin, insan yorumunun derinliğini koruyarak metodolojik bütünlüğü muhafaza ederken analiz sürecini hızlandırdığı ve ölçeklendirdiği gözlemlenmiştir. Bu çalışma, yapay zeka destekli tematik analizin sosyal bilimlerde nitel araştırma için yenilikçi ve verimli bir metodolojik çerçeve sunma potansiyelini göstermektedir.

Anahtar Kelimeler

• Türk tarımı
• tematik analiz
• yapay zekâ
• hibrit model
• nitel araştırma

The Productivity Paradox in Turkish Agriculture: Can AI-Supported Thematic Analysis Provide a Solution?/ Türk Tarımında Verimlilik Paradoksu: Yapay Zekâ Destekli Tematik Analiz Bir Çözüm Sağlayabilir mi?

Öz

This study addresses the time cost problem, one of the most significant limitations of thematic analysis among qualitative research methods, and proposes an AI-supported hybrid model to address this issue. The focus of the research is the “productivity paradox” (low productivity despite abundant resources) in the Turkish agricultural sector. In the study, Braun and Clarke’s (2006) traditional six-step thematic analysis protocol and Keshav’s (2007) three-pass systematic reading model were integrated with AI to develop a methodological framework. A total of 985 academic articles collected from the Web of Science (WoS) database using the keywords “Turkey” and “agriculture” were analyzed using two different large language models (LLMs): ChatGPT and DeepSeek. In the first stage, the 268 articles flagged by both models as “high relevance” were identified as the intersection set, and in-depth analysis was conducted on this dataset. In the second and third stages, AI capabilities such as code suggestion, thematic clustering, and consistency analysis were utilized; however, the final interpretation, synthesis, and meaning processes were carried out by human researchers. The findings revealed that the productivity paradox in Turkish agriculture stems from interconnected multidimensional themes such as natural resource management, technological adaptation, climate change, policy governance, socio-economic dynamics, and water management. It was observed that the proposed hybrid model accelerated and scaled the analysis process while maintaining methodological integrity by preserving the depth of human interpretation. This study demonstrates the potential of AI-supported thematic analysis to offer an innovative and efficient methodological framework for qualitative research in the social sciences.

Anahtar Kelimeler

• Turkish agriculture
• thematic analysis
• artificial intelligence
• hybrid model
• qualitative research

Kaynakça

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