Japanese Literature, Culture, and Language Job Market (1966-2025)

Showing: US & Canada (3,460 listings)
Individual Tag Trends
Top 25 Hiring Institutions (confirmed)
Top 15 Institutions by Decade
Decade Breakdown
Data Sources
Period Focus (confirmed)
About This Visualization
Methodology

This visualization presents 60 years of job market data for Japanese Literature and Culture positions in higher education (1966–2025).

Data sources (8):

  • MLA Job Information List — OCR'd JSTOR PDFs (1,729 listings, 1966–2017)
  • MLA JIL XLSX — PI hand-coded extraction (1,072 listings, 1967–2017)
  • Paula Curtis Job Market Spreadsheets (1,063 listings, 2023–2025)
  • Academic Jobs Wiki / Fandom (167 listings + 325 hiring outcomes, 2012–2020)
  • Academic Wiki XLSX (144 listings)
  • AATJ Job Board (51 listings)
  • H-Japan email list (44 listings)
  • MLA JIL CSV (10 listings)

Key findings: Peak hiring occurred 1985–1992 during the Japan economic bubble (averaging 120+ listings/year). A sharp cliff in 1992–93 reduced postings to under 30/year for the remainder of the 1990s. Recent years (2023–2025) show strong recovery to 300+ listings/year, though this partly reflects broader data capture (including international positions) via Paula Curtis’s tracking project. Use the “North America only” filter for a more consistent comparison with the historical MLA JIL data.

North America filter: The MLA JIL data (1966–2017) covers almost exclusively US and Canadian institutions. The Curtis data (2023–2025) tracks positions worldwide, including ~490 Japan-based positions. To enable apples-to-apples comparison across the full 60-year span, use the “North America only” toggle, which restricts to US and Canadian listings.

Relevance classification: Each listing is classified as confirmed (clearly Japanese literature/culture), review_needed (mentions Japan but may be language-only or tangential), or excluded (not relevant). The visualization shows both confirmed and review_needed listings by default, with a toggle to show confirmed only.

Discipline tags: Each listing receives zero or more of 14 tags (literature, language, culture, modern, premodern, history, media, film, translation, drama, poetry, anime, manga, videogames) based on keyword matching against the listing title and description text. This is simple substring/keyword matching, not machine learning or transformer-based classification. Tags are multi-valued: a single listing may have 3–5 tags.

Tenure-track status: Extracted from position title and description keywords. ~45% are tenure-track, ~30% non-tenure-track, ~25% unknown (especially in older OCR'd listings where full position details are not captured).

Institution normalization: A 180-entry dictionary normalizes institution names across OCR variants, abbreviations, and multi-campus systems. “University of California” aggregates all UC campuses when campus is not specified in the original listing.

Historical annotations: Reference lines mark key economic and social events that affected hiring: the 1988 Japan bubble peak, the 1992 post-bubble collapse, the 2008 financial crisis, and the 2020–2022 COVID-19 pandemic.