# Fixed JobQueue Implementation with Proper Locking

The race condition in the original code arises from unsynchronized access to shared data structures (`self.jobs` and `self.results`) across multiple threads. To fix this, I've added a threading lock to ensure that all accesses to shared state are atomic and thread-safe. The processor function is executed outside the critical section to allow concurrent job processing while still protecting shared resources.

```python
import threading

class JobQueue:
    def __init__(self):
        self.jobs = {}
        self.results = {}
        self.lock = threading.Lock()

    def add_job(self, job_id, data):
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        with self.lock:
            if job_id not in self.jobs:
                return False
            data = self.jobs[job_id]
        
        # Process outside the lock to allow concurrent execution
        result = processor(data)

        with self.lock:
            self.results[job_id] = result
            del self.jobs[job_id]
        
        return True

    def get_result(self, job_id):
        with self.lock:
            return self.results.get(job_id)
```

### Key Fixes:

1. **Added a Lock:** `self.lock = threading.Lock()` is initialized in `__init__` to protect shared state.
2. **Protected Critical Sections:** All accesses to `self.jobs` and `self.results` are now within `with self.lock:` blocks.
3. **Minimized Lock Duration:** The processor function runs outside the lock to allow concurrent execution of job processing.
4. **Atomicity of Job Removal and Result Storage:** Both the result assignment and job removal occur within the same lock to ensure consistency.

This implementation prevents data races and ensures thread-safe access to the job queue and results dictionary.