Cited by 51

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(Mis)use of scientific measurements in forensic science

Dror, Itiel E., Scurich, Nicholas·2020·Forensic Science International: Synergy

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Research works printed but not linked: 31

  1. Garrett, B.L. (in press). Autopsy of a Crime Lab. University of California Press..
  2. Howes, 2015, The communication of forensic science in the criminal justice system: a review of theory and proposed directions for research, Sci. Justice, 55, 145, 10.1016/j.scijus.2014.11.002 · DOI
  3. Lyon, 1996, Relevance ratio: evaluating the probative value of expert testimony in child sexual abuse cases, Cornell Law Rev., 82, 43
  4. Sangero, 2018, Safety from flawed forensic sciences evidence, Ga. State Univ. Law Rev., 34, 1129
  5. Haber, 2014, Experimental results of fingerprint comparison validity and reliability: a review and critical analysis, Sci. Justice, 54, 375, 10.1016/j.scijus.2013.08.007 · DOI
  6. Saks, 2005, The coming paradigm shift in forensic identification science, Science, 309, 892, 10.1126/science.1111565 · DOI
  7. Koehler, 2013, Proficiency tests to estimate error rates in the forensic sciences, Law Probab. Risk, 12, 89, 10.1093/lpr/mgs013 · DOI
  8. Cole, 2005, More than zero: accounting for error in latent fingerprint identification, J. Crim. Law Criminol., 95, 985
  9. Garrett, 2009, Invalid forensic science testimony and wrongful convictions, Va. Law Rev., 95, 1
  10. Murrie, 2019, Perceptions and estimates of error rates in forensic science, Forensic Sci. Int., 302, 10.1016/j.forsciint.2019.109887 · DOI
  11. Daubert, 1993, vol. 509, 579
  12. Meixner, 2014, The hidden Daubert factor: how judges use error rates in assessing scientific evidence, Wis. Law Rev., 1063
  13. Gatowski, 2001, Asking the gatekeepers: a national survey of judges on judging expert evidence in a post-Daubert world, Law Hum. Behav., 25, 433, 10.1023/A:1012899030937 · DOI
  14. United States v, 2001, Havvard, 260
  15. Capra, 2018, Symposium on forensic expert testimony, Daubert, and Rule 702, Fordham Law Rev., 86, 1463
  16. Dror, 2019, Cannot Decide": the fine line between appropriate inconclusive determinations VS. unjustifiably deciding not to decide, J. Forensic Sci., 64, 1, 10.1111/1556-4029.13854 · DOI
  17. McQuiston-Surrett, 2009, The testimony of forensic identification science: what expert witnesses say and what factfinders hear, Law Hum. Behav., 33, 436, 10.1007/s10979-008-9169-1 · DOI
  18. Dror, 2020, The Error in ‘Error Rate’: why error rates are so needed, yet so elusive, J. Forensic Sci., 65, 1034, 10.1111/1556-4029.14435 · DOI
  19. Kelley, 2020, How do latent print examiners perceive proficiency testing? An analysis of examiner perceptions, performance, and print quality, Sci. Justice, 60, 120, 10.1016/j.scijus.2019.11.002 · DOI
  20. Koehler, 2008, Fingerprint error rates and proficiency tests: what they are and why they matter, Hastings Law J., 59, 1077
  21. Pierce, 2020, Development and implementation of an effective blind proficiency testing program, J. Forensic Sci., 65, 809, 10.1111/1556-4029.14269 · DOI
  22. Jeanguenat, 2018, Human factors effecting forensic decision making: workplace stress and wellbeing, J. Forensic Sci., 63, 258, 10.1111/1556-4029.13533 · DOI
  23. Dror, 2020, Cognitive and human factors in expert decision making: six fallacies and the eight sources of bias, Anal. Chem., 92, 7998, 10.1021/acs.analchem.0c00704 · DOI · Paper page
  24. Cole, 2018, Fingerprints and miscarriages of justice: “other” types of error and a post-conviction right to database searching, Albany Law Rev., 81, 807
  25. Ulery, 2012, Repeatability and reproducibility of decisions by latent fingerprint examiners, PLoS, 7
  26. Langenburg, 2009, Testing for Potential contextual bias effects during the verification stage of the ACEV methodology when conducting fingerprint comparisons, J. Forensic Sci., 54, 571, 10.1111/j.1556-4029.2009.01025.x · DOI
  27. Keisler, 2018, Isolated pairs research study, AFTE Journal, 50, 56
  28. Lyons, 2009, The identification of consecutively manufactured extractors, AFTE Journal, 41, 246
  29. Dror, 2016, A hierarchy of expert performance (HEP), Journal of Applied Research in Memory and Cognition, 5, 121, 10.1016/j.jarmac.2016.03.001 · DOI · Paper page
  30. Bunch, 2003, A comprehensive validity study for the forensic examination of cartridge cases, AFTE Journal, 35, 201
  31. Smith, 2005, Cartridge case and bullet comparison validation study with firearms submitted in casework, AFTE Journal, 37, 130

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Metadata

Authors
Dror, I. E., & Scurich, N.
Venue
Forensic Science International: Synergy
Publisher
Elsevier BV
Published
2020
Volume
2
Pages
333-338
ISSN
2589-871X
Cited by
51
References
2
Language
en
Funders
  • Carnegie Mellon University
  • University of Virginia
  • Duke University
  • National Institute of Standards and Technology
  • Swarthmore College
  • University of California, Irvine
  • West Virginia University
  • University of Pennsylvania

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