Who Taught the Teachers · Episode 6 of 12 · 21 min
Conduct Unbecoming
By the early 1930s, roughly three-quarters of surveyed urban districts refused to hire a married woman, and about half fired women teachers who married. The episode examines moral surveillance of American teachers, separating the record from viral fake contract lists. It covers marriage bars, Cold War loyalty oaths, Florida's Johns Committee, and California's 1978 defeat of the Briggs Initiative.
Who's who
- Willard WallerSociologist who reproduced a real teacher contract in 1932
- Claudia Goldin+2 more episodesEconomic historian who reconstructed how widespread marriage bars were
- Irving AdlerNew York teacher forced out; plaintiff in Adler v. Board of Education
- Harry KeyishianBuffalo instructor whose case struck down New York's Feinberg Law
- Charley JohnsFlorida senator whose committee hunted gay and lesbian teachers
- Anita BryantSinger whose 1977 campaign made removing gay teachers a national cause
- John BriggsCalifornia senator behind Proposition 6 in 1978
- Harvey MilkSan Francisco supervisor who campaigned against Proposition 6
Where it happens
- New York City+13 more episodesBoard of Education questioned over 1,000 teachers in the 1950s
- Florida+3 more episodesJohns Committee revoked 71 teaching certificates
- California+30 more episodesVoters rejected Proposition 6 in 1978
Timeline
- June 30, 1932Section 213 of the Economy Act forces one spouse of each federal couple out of work
- 1942Marriage bars peak in surveyed districts
- March 3, 1952Supreme Court upholds New York's Feinberg Law in Adler
- 1956-1965Florida's Johns Committee investigates teachers; claims 71 certificates revoked
- January 23, 1967Keyishian v. Board of Regents strikes down the Feinberg Law
- November 7, 1978California rejects Proposition 6, 58.4 to 41.6 percent
Still debated
The viral lists of old teacher rules appear fabricated, yet real documents show restrictions of the same kind. Purge counts are minimums, and how much surveillance shaped who entered teaching is a well-grounded inference rather than a statistic.
