"Get Your Gavel Out of My Pants": Replacing the Medical and Legal Scrutiny of Trans Bodies with Self-Identification as a Basis for Changing Sex Markers on Birth Certificates

10.26686/wgtn.17009615 ◽

2021 ◽

Author(s):

◽

Emily Blincoe

Keyword(s):

Medical Model ◽

Birth Certificate ◽

The Self ◽

Family Court ◽

Birth Certificates ◽

Medical Evidence ◽

Trans People ◽

Sex Marker ◽

The Family ◽

Identification Model

<p>Section 28 of the Births, Deaths, Marriages, and Relationships Registration Act 1995 allows people to apply to the Family Court to change the sex marker on their birth certificate. This essay argues that this provision is out-dated and does not serve the needs of the trans community. It is based on the medical model of sex, and requires medical evidence that the applicant’s body conforms sufficiently to that of the “nominated sex”. This essay suggests a reform based on the self-identification model, which exists in Argentina for birth certificates, and in New Zealand for passports and drivers’ licences. Such a reform of s 28 would bring birth certificates in line with these other documents, leading to more consistency and increased respect for the human rights of trans people.</p>

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Sex Markers on Birth Certificates: Replacing the Medical Model with Self-Identification

Victoria University of Wellington Law Review ◽

10.26686/vuwlr.v46i1.4935 ◽

2015 ◽

Vol 46 (1) ◽

pp. 57

Author(s):

Emily Blincoe

Keyword(s):

Medical Model ◽

Birth Certificate ◽

Family Court ◽

Birth Certificates ◽

Legal Recognition ◽

Medical Interventions ◽

Trans People ◽

Sex Marker ◽

The Family ◽

Judicial Expertise

Section 28 of the Births, Deaths, Marriages, and Relationships Registration Act 1995 allows people to apply to the Family Court to change the sex marker on their birth certificate. This article argues that this provision is outdated and does not go far enough in providing legal recognition for trans people. It is based on the medical model of sex, and requires medical evidence that the applicant's body conforms sufficiently to that of the "nominated sex". The major problem with this requirement is that the required medical interventions are unavailable or undesirable for many trans people, so should not be a basis for legal recognition. The medical model also privileges medical and judicial expertise above a person's own identity and experience. This article suggests a reform based on the self-identification model, which exists in Argentina for birth certificates, and in New Zealand for passports and drivers' licences. Such a reform of s 28 would bring birth certificates in line with these other documents, leading to more consistency and increased respect for the human rights of trans people.

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PENENTUAN DAERAH PRIORITAS PELAYANAN AKTA KELAHIRAN DENGAN METODE K-NN DAN K-MEANS

Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika ◽

10.33751/komputasi.v17i1.1735 ◽

2020 ◽

Vol 17 (1) ◽

pp. 319-328

Author(s):

Ade Muchlis Maulana Anwar ◽

Prihastuti Harsani ◽

Aries Maesya

Keyword(s):

Nearest Neighbor ◽

Information Gain ◽

Birth Certificate ◽

Population Data ◽

Community Services ◽

Birth Certificates ◽

Similar Data ◽

K Nearest Neighbor ◽

Civil Registration ◽

The Family

Population Data is individual data or aggregate data that is structured as a result of Population Registration and Civil Registration activities. Birth Certificate is a Civil Registration Deed as a result of recording the birth event of a baby whose birth is reported to be registered on the Family Card and given a Population Identification Number (NIK) as a basis for obtaining other community services. From the total number of integrated birth certificate reporting for the 2018 Population Administration Information System (SIAK) totaling 570,637 there were 503,946 reported late and only 66,691 were reported publicly. Clustering is a method used to classify data that is similar to others in one group or similar data to other groups. K-Nearest Neighbor is a method for classifying objects based on learning data that is the closest distance to the test data. k-means is a method used to divide a number of objects into groups based on existing categories by looking at the midpoint. In data mining preprocesses, data is cleaned by filling in the blank data with the most dominating data, and selecting attributes using the information gain method. Based on the k-nearest neighbor method to predict delays in reporting and the k-means method to classify priority areas of service with 10,000 birth certificate data on birth certificates in 2019 that have good enough performance to produce predictions with an accuracy of 74.00% and with K = 2 on k-means produces a index davies bouldin of 1,179.

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Alleviating Anxiety and Cultivating Care: Young Trans People in the Family Court of Australia

Australian Feminist Law Journal ◽

10.1080/13200968.2019.1636752 ◽

2019 ◽

Vol 45 (1) ◽

pp. 111-130

Author(s):

Senthorun Raj

Keyword(s):

Family Court ◽

Trans People ◽

The Family

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PENENTUAN DAERAH PRIORITAS PELAYANAN AKTA KELAHIRAN DENGAN METODE K-NN DAN K-MEANS

Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika ◽

10.33751/komputasi.v17i1.1884 ◽

2020 ◽

Vol 17 (1) ◽

pp. 319-328

Author(s):

Ade Muchlis Maulana Anwar ◽

Prihastuti Harsani ◽

Aries Maesya

Keyword(s):

Nearest Neighbor ◽

Information Gain ◽

Birth Certificate ◽

Population Data ◽

Community Services ◽

Birth Certificates ◽

Similar Data ◽

K Nearest Neighbor ◽

Civil Registration ◽

The Family

Population Data is individual data or aggregate data that is structured as a result of Population Registration and Civil Registration activities. Birth Certificate is a Civil Registration Deed as a result of recording the birth event of a baby whose birth is reported to be registered on the Family Card and given a Population Identification Number (NIK) as a basis for obtaining other community services. From the total number of integrated birth certificate reporting for the 2018 Population Administration Information System (SIAK) totaling 570,637 there were 503,946 reported late and only 66,691 were reported publicly. Clustering is a method used to classify data that is similar to others in one group or similar data to other groups. K-Nearest Neighbor is a method for classifying objects based on learning data that is the closest distance to the test data. k-means is a method used to divide a number of objects into groups based on existing categories by looking at the midpoint. In data mining preprocesses, data is cleaned by filling in the blank data with the most dominating data, and selecting attributes using the information gain method. Based on the k-nearest neighbor method to predict delays in reporting and the k-means method to classify priority areas of service with 10,000 birth certificate data on birth certificates in 2019 that have good enough performance to produce predictions with an accuracy of 74.00% and with K = 2 on k-means produces a index davies bouldin of 1,179

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PENENTUAN DAERAH PRIORITAS PELAYANAN AKTA KELAHIRAN DENGAN METODE K-NN DAN K-MEANS

Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika ◽

10.33751/komputasi.v17i1.1746 ◽

2020 ◽

Vol 17 (1) ◽

pp. 319-328

Author(s):

Ade Muchlis Maulana Anwar ◽

Prihastuti Harsani ◽

Aries Maesya

Keyword(s):

Nearest Neighbor ◽

Information Gain ◽

Birth Certificate ◽

Population Data ◽

Community Services ◽

Birth Certificates ◽

Similar Data ◽

K Nearest Neighbor ◽

Civil Registration ◽

The Family

Population Data is individual data or aggregate data that is structured as a result of Population Registration and Civil Registration activities. Birth Certificate is a Civil Registration Deed as a result of recording the birth event of a baby whose birth is reported to be registered on the Family Card and given a Population Identification Number (NIK) as a basis for obtaining other community services. From the total number of integrated birth certificate reporting for the 2018 Population Administration Information System (SIAK) totaling 570,637 there were 503,946 reported late and only 66,691 were reported publicly. Clustering is a method used to classify data that is similar to others in one group or similar data to other groups. K-Nearest Neighbor is a method for classifying objects based on learning data that is the closest distance to the test data. k-means is a method used to divide a number of objects into groups based on existing categories by looking at the midpoint. In data mining preprocesses, data is cleaned by filling in the blank data with the most dominating data, and selecting attributes using the information gain method. Based on the k-nearest neighbor method to predict delays in reporting and the k-means method to classify priority areas of service with 10,000 birth certificate data on birth certificates in 2019 that have good enough performance to produce predictions with an accuracy of 74.00% and with K = 2 on k-means produces a index davies bouldin of 1,179.

Download Full-text