GitHub - elmiram/anime-professions: Which real-world jobs does anime actually show? ~22k titles classified to ISCO-08. Live explorer + dataset. · GitHub

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Anime Professions

Which real-world jobs does anime actually show? This project scans 21,988 anime and<br>counts how often each occupation appears — a complete lookup table keyed to ISCO-08 , the<br>UN/ILO's standard occupation classification. The headline finding is the absence list: the<br>jobs anime never names at all.

🔎 Live explorer: https://elmiram.github.io/anime-professions/  ·  search any profession, see which anime feature it, and how it trends by decade.

📊 The deck: a 12-slide fun tour of the findings (PDF).

A few things it found:

Teachers are the most-named profession (in ~1,370 anime, 7.6%) — anime lives at school.

In the 2020s, shopkeepers overtook teachers for #1, on the back of the isekai/slice-of-life boom.

If you count fantasy roles, being royalty (1,792 titles) out-appears every real job.

Of the ~150 jobs a character could plausibly be named as, anime never names 17 — even across<br>millions of lines of dialogue: financial analysts, systems analysts, driving instructors, printers,<br>fast-food workers, ships' engineers… the back-office and service tail. (See The occupation list below.)

Your niche jobs, quantified: software developers 41 , insurance reps 18 (Trigun's leads are<br>literally insurance agents), pharmacists 15 , tram/bus drivers 5 .

What this is

A hobby data project. It builds a taxonomy of occupations, detects mentions of them in anime,<br>classifies each mention with an LLM, and produces per-occupation counts, a decade-by-decade<br>breakdown, precision/recall estimates, and three CSVs. A small local/static web app explores the<br>results.

Real occupations are kept strictly separate from a fantasy stratum (ninja, mage, adventurer,<br>royalty, samurai…) — the fictional roles are counted but never folded into the real-job totals.

How it works

The occupation list — 436 trimmed to ~150. Occupations are classified against ISCO-08 , the<br>UN/ILO's list of 436 official job codes . Most of those can't be named in a story — nobody<br>announces "I'm a mineral-processing-plant operator" — so a zero for them would say nothing about<br>anime. So the list is trimmed to the ~150 jobs a character could plausibly be identified as (the<br>"renderable" set); the other ~286 are reported as untestable, not absent . Anime names 133 of the<br>150 — the 17 it never names are the headline finding, because they could plausibly appear yet<br>don't. (Every anime is still scanned for all ~150; the trimming is of job categories, not titles.)

Detection itself is text-first, in three layers:

Corpus + protagonists — every anime's plot summary (via AniList).

Supporting characters — AniList character bios, with a role field (main / supporting /<br>background) so a doctor who appears in six episodes still counts as representation.

Dialogue — ~128,000 Japanese subtitle files (from a community mirror), so a job mentioned<br>only in passing gets caught too.

Detection is a two-stage pipeline:

Stage 1 — high recall. A keyword lexicon (English + Japanese surface forms mapped to ISCO<br>codes) casts a wide net over the text, accepting false positives.

Stage 2 — precision. Every ambiguous candidate sentence is judged in context by an LLM<br>(Claude Haiku ): does a character here really hold this job? This kills the classic traps —<br>negation ("I'm not a doctor"), hypotheticals ("if I were a pilot"), polysemy ("officer",<br>"band"), and fantasy homonyms (a mecha pilot is not an airline pilot).

A stronger model (Claude Opus ) then grades a stratified sample of the confirmed...