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FAMILY HISTORY TECHNOLOGY WORKSHOP February 3, 2012

This study compares A-B-Arbitrate and Peer Review models in indexing efficiency and quality, utilizing historical data analysis and field experiments. Findings reveal insights into capturing differences and improving accuracy. Future possibilities include algorithmic peer review and initial indexing.

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FAMILY HISTORY TECHNOLOGY WORKSHOP February 3, 2012

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  1. Improving Indexing Efficiency & Quality:Comparing A-B-Arbitrate and Peer Review FAMILY HISTORY TECHNOLOGY WORKSHOPFebruary 3, 2012 Derek Hansen, Jake Gehring, Patrick Schone, and Matthew Reid

  2. FamilySearch

  3. FamilySearch indexing

  4. A-b-arbitrate process (a-b-arb) A ARB B

  5. The problem

  6. Our approach • Historical Data Analysis • Field Experiment comparing quality control models

  7. Historical data analysis • Quality (estimated based on A-B agreement) • Measures difficulty more than actual quality • Underestimates quality, since an experienced Arbitrator reviews all A-B disagreements • Good at capturing differences across people, fields, and projects • Time (calculated using keystroke-logging data) • Idle time is tracked separately, making actual time measurements more accurate • Outliers removed

  8. A-B Agreement by field

  9. A-b agreement by language English Language French Language Given Name: 62.7% Surname: 48.8% 1871 Canadian Census • Given Name: 79.8 • Surname: 66.4

  10. A-b agreement by experience Birth Place: All U.S. Censuses B (novice ↔ expert) A (novice ↔ expert)

  11. A-b agreement by experience Given Name: All U.S. Censuses B (novice ↔ expert) A (novice ↔ expert)

  12. A-b agreement by experience Surname: All U.S. Censuses B (novice ↔ expert) A (novice ↔ expert)

  13. A-b agreement by experience Gender: All U.S. Censuses B (novice ↔ expert) A (novice ↔ expert)

  14. A-b agreement by experience Canada - English U.S. - English Mexico - Spanish Canada - French

  15. Time & keystroke by experience

  16. Time & Keystroke of ARB

  17. A new approach? (A-R-ARB) • Peer review model • Efficiency ++ • Quality ?

  18. Peer review process (A-R-ARB) A R ARB Already Filled In Optional?

  19. Field Experiment • Develop Truth Set of 2,000 1930 Census images • Use historical A-B-ARB data • Create new A-R-ARB dataset by having new indexers review and arbitrate • Compare quality & efficiency • Qualitatively identify types of errors

  20. Discussion IMPLICATIONS • Transition users from novice to expert • Recruit foreign language indexers • Intelligent matching based on expertise (in A-B-ARB &/or A-R-ARB) FUTURE POSSIBILITIES • Peer review by algorithms? • Initial indexing by algorithms?

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