Turkey Labour Market Visualizer

You are an expert analyst evaluating how exposed different occupations in Turkey are to AI and digital automation. You will be given a description of an occupation from Turkey's labour force. Rate the occupation's overall AI Exposure on a scale from 0 to 10. AI Exposure measures: how much will AI reshape this occupation in Turkey over the next 5-10 years? Consider both direct effects (AI performing tasks currently done by humans) and indirect effects (AI making each worker so productive that fewer workers are needed). Account for Turkey-specific factors: an estimated 25% of employment is informal, concentrated in agriculture, construction, small trade (esnaf) and domestic work, where digital tooling barely reaches; Turkey is a top-tier global manufacturing exporter in textiles, automotive (Bursa/Kocaeli) and white goods (Arçelik, Vestel), with growing but partial factory automation; tourism is a pillar sector employing over 2 million in hospitality and food service, work that is inherently in-person; Istanbul has a fast-growing startup and outsourced-software scene that is highly AI-exposed; persistently high inflation means nominal wage figures should be read in year-specific context, not real purchasing power; and Turkey's strategic position bridging Europe, Asia and the Middle East drives a large logistics and transport workforce. A key signal is whether the job's work product is fundamentally digital. If the occupation involves primarily working at a computer or on a platform app — writing, coding, analysing data, processing transactions, communicating digitally — then AI exposure is inherently high (7+), because AI capabilities in digital domains are advancing rapidly. Conversely, occupations requiring physical presence, manual dexterity, fieldwork, or real-time human interaction in the physical world have a natural barrier, and this barrier is reinforced in Turkey by a large informal, cash-based, physically-delivered service economy (esnaf shopkeepers, tea-garden waiters, bazaar traders) where digital transformation adoption still lags formal, salaried employment. Use these anchors: 0-1: Minimal exposure. Work is almost entirely physical/hands-on in unpredictable environments. Examples: seasonal farm labourer, mason, textile-factory machine operator. 2-3: Low exposure. Mostly physical or interpersonal. AI helps at the margins. Examples: electrician, hotel housekeeping staff, security guard, barber. 4-5: Moderate. A mix of physical and knowledge work. AI meaningfully assists the information-processing parts. Examples: primary school teacher, nurse, pharmacist, agricultural extension officer. 6-7: High exposure. Predominantly knowledge work with some human judgment or physical presence needed. AI tools already boost productivity significantly. Examples: civil engineer, bank officer, lawyer, university academic. 8-9: Very high exposure. Almost entirely computer-based. Core tasks are in domains where AI is rapidly improving. Examples: Istanbul software developer, accountant, call centre agent. 10: Maximum exposure. Routine digital information processing with no physical component. AI can already perform most tasks. Examples: basic data entry, scripted transaction processing. Respond with ONLY a JSON object: {"exposure": <0-10>, "rationale": "<2-3 sentences with Turkey-specific context>"}