Job Listing Scraper and Tracker

You are an expert web scraper specialising in job-board data extraction and normalisation.

Write a complete, production-ready Python scraper that collects job postings from {{target-url}} and outputs them into a structured CSV tracker with the following columns:

- role_title
- company
- location (normalised to "City, Country" or "Remote")
- remote_status (one of: Full Remote | Hybrid | On-site | Not Specified)
- salary_range (normalised to "$min–$max currency/period" or "Not Listed")
- posting_date (ISO 8601 format YYYY-MM-DD, inferred if relative like "2 days ago")
- application_link (absolute URL)
- listing_id (unique stable identifier derived from the posting to enable deduplication)
- status (Active | Closed)
- last_seen (ISO 8601 timestamp of last successful scrape)

## Functional requirements

1. **Deduplication logic**: use listing_id to detect reposts. If a job with the same listing_id already exists in the tracker, update last_seen but do not create a duplicate row.
2. **Closure detection**: after each scrape, compare the current set of listing_ids with the previous run. Mark any job no longer present on the page as status=Closed and record the timestamp.
3. **Location normalisation**: parse free-text location strings into standardised "City, Country" format. Handle common variations ("SF" → "San Francisco, USA"; "London, UK" → "London, United Kingdom"). If the listing explicitly mentions remote work in the location field, set location to "Remote" and remote_status to "Full Remote".
4. **Salary normalisation**: extract salary data and convert into a consistent "$min–$max USD/year" format. Handle ranges, single figures, hourly/monthly rates (convert to annual assuming full-time), currency symbols (£, €, $), and abbreviations (k for thousands). If no salary is published, write "Not Listed".
5. **Robust parsing**: inspect the page's DOM structure—check for JSON-LD, data attributes, or server-rendered HTML—and extract fields accordingly. Use {{scraping-technique}} (requests + BeautifulSoup, Playwright for JavaScript-heavy pages, or Selenium if interaction is required).
6. **Persistence**: on first run, create a new CSV. On subsequent runs, read the existing tracker, merge updates, mark closures, and write back.
7. **Error handling**: log warnings for unparseable dates or salaries; fall back to raw text in a notes column if critical fields cannot be extracted.

Include inline comments explaining deduplication logic, normalisation heuristics, and closure-marking flow. Add a `if __name__ == "__main__":` block that runs the scraper and prints a summary: jobs added, updated, closed.