Here's the corrected implementation of the `JobQueue` class with proper concurrency control to eliminate race conditions:

```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

            # Remove job from queue to prevent double processing
            data = self.jobs[job_id]
            del self.jobs[job_id]
        
        # Process the job outside the lock to allow concurrent execution
        result = processor(data)
        
        # Store result under lock to prevent race conditions
        with self.lock:
            self.results[job_id] = result
        
        return True

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

### Key Fixes:
1. **Added a `threading.Lock`** in `__init__` to synchronize access to shared resources.
2. **Protected all shared state access** (`self.jobs`, `self.results`) with `with self.lock:` blocks.
3. **Prevented double processing** by removing the job from `self.jobs` before releasing the lock.
4. **Improved concurrency** by releasing the lock during the CPU-intensive `processor(data)` call.
5. **Ensured safe result storage** by acquiring the lock before writing to `self.results`.

This implementation avoids race conditions while allowing multiple jobs to be processed concurrently, ensuring correctness and scalability in multi-threaded environments.