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NON-BINARY LIFE CONDITIONS

STUDIES: APPLYING GENDER AS METHODOLOGY

Luana Batista-Goulart

luana.batista-goulart@cepam.cnrs.fr

EAA 2019

1

Agenda

2

1st "poster-distributing» woman, septembre 1908 : [photographie

de presse] / [Agence Rol]Bibliothèque Nationale de France

Gender and Inequalities: Introduction

Methodological implications

Examples

GENDER AND INEQUALITIES

Introduction

3 Sex

ʃFluid

ʃVariable

ʃDepending on the society and time.

ʃConcerns culturalaspects that may

affect physical development and well- being.

ʃImportant role in determining food

access which, in its turn, affects nutritional status.

Gender

ʃExact

ʃInvariable

ʃEqual in all societies and time

ʃSocially constructed

ʃConcerns biological aspects, but can be

influenced by culture and environment:

ʃStature

ʃNutritional status

4

Inequalities:

ʃDifferential structural conditions, and

power to control material and human resources or information (Granados, in press);

ʃSocial differentiation, followed by

different moral evaluation (Fournier et al,

2015);

ʃGender, occupation, origin, religion, etc.

Intersectionality:

ʃInteraction of multiple causes of

inequalities;

Diet and nutrition

ʃSuitable to identify gender and social

inequalities;

ʃBiosocial approach: interaction of

biological and social aspects;

ʃExtrapolates nutritional needs and

differentiates groups according to age, gender, religion or social status, for example;

ʃInfluences growth and development;

ʃCan be one of the causes of biological

plasticity. blog post, how this stranger decided she was fat. She would file the post

Chimanda N. Adiche, Americanah (2016)

5

Le déjeuner des canotiers (1875), Renoir

Le Repas (1891), Gauguin

6

Racial map of Brazil (patadata.org)

ʃWhite population

ʃBrown population

ʃBlack population

ʃYellow population

ʃNative population

Poverty map Brazil -2017 (IBGE)

(proportion)

ʃ26.5% -less than US$ 5.5 / day

ʃRegions with non-white

majority: highest concentration of people living below the poverty line

ʃAmong single brown women

with children -64.4%

ʃAmong single white women

with children -41.5%

ʃCouples with children -30.4%

ʃCouples with no children -10%

Poverty map 2017 (IBGE)

South less than 15% under poverty line

North more than 45% under poverty line

Inequalities

Gender bias in food access

ʃGinnaio (2011)

ʃPellagra epidemics -caused by lack of

vitamin B3

ʃItaly (18thto 20thcenturies)

ʃMost affected: women from lower social

classesliving in the countryside

ʃMeat was reserved to men

ʃMen had preferential access to medical

treatments

ʃHigher mortality among females

ʃAmbrose (et al, 2003)

ʃUSA (1050 1150 BC)

ʃFemale from lower social classes presented

more stress markers than the other groups

ʃWhite (2005)

ʃBelize (1250 BC250 AD, 15201670 AD))

ʃDiet reconstruction -stable isotopes analysis

ʃMen -higher consumption of animal proteins

ʃFemale ate more varied sources of proteins

ʃLarger gender differences in lower social

classes 7

Marseille, Senegalese [carrying a

dish of food and smoking a pipe](1914), Gallica

METHODOLOGICAL IMPLICATIONS

8

Studies of stress markers/life conditions

ʃFemale/male differences

ʃAge/period/site

ʃMarkers analysed separately

ʃUbelaker and Pap, 2009; Kacki, 2016,

ʃSocial status based on funerary context

ʃFemale/male differences

ʃAmbrose et al, 2003; Vercelotti, 2011

ʃFemale/males differences

ʃCorruccini et al, 1982; Kelly and Angel, 1987;

ʃImportance of comparing

female/male conditions

ʃKnowing

ʃHowever:

ʃBinary division of the sample

ʃHide other social aspects influence life

conditions

What to do when we cannot obtain

information about social status fromthe archaeologicalcontext? 9

Data analysis

ʃFemur dimension analysis aiming to

identify nutritional differences ʃa priori

ʃSample:95 adults

ʃMethods:

ʃFactorial analysis of mixed data and

hierarchical clustering

ʃVariables: femur length, femur diameter

and sex

ʃMass graves of victims of the Great

Plague of Marseilles(1720-1722);

ʃPrimary multiple tombs;

ʃNot possible to identify social status;

ʃLocated at the city of Martigues

(Provence -France) (Tzortzis, 2009);

ʃSuffered several starvation episodes

before the epidemic (Séguy, 2016).

Image: Stéfan Tzortzis (2009: 137).

Martigues and Marseilles-source

package R: ggmaps 10

ʃVariables: femur length and diameter

ʃMixed groups

ʃFemales concentrated in group 3

ʃMales concentrated in group 4

FF?MM?INDTotal

Group 11225010

Group 26486125

Group 325911441

Group 411134120

Total33162416695

ʃVariables: femur length and diameter, sex (4

categories) ʃFemales and probable females together (g. 2 and 3)

ʃMales and probable males separately

ʃJust 1 male grouped with females

FF?MM?INDTotal

Group 1000607

Group 217910027

Group 315600021

Group 411140016

Group 500010010

Group 6009009

Total33162416089FF?MM?INDTotal

Group 10066013

Group 2181000028

Group 315600021

Group 4001810028

Total33162417089

ʃVariables: femur length and diameter, binary sex

ʃAll females together

ʃAll males together

ʃNo individuals grouped with other sex

11

Results

Different groups composition

Adding sex as variable forces a binary division

ʃCribra orbitalia (n=61)

ʃAffected mainly non-adults

20-30 y30-40 y40-50 y50 + y

Fem377212251

Male51832594

p-value0,059440,60450,017290,4796 12

ʃLinear enamel hypoplasia(n=72)

ʃMore males affected than females;

ʃNo differences between ages

Stress markers

FemaleMaleIndImmature

Affected3430125

Non-aff74024

ʃPorotic hyperostosis of cranial vault (n=86)

ʃFemales and males affected similarly

ʃDifferences between age groups (20-30y more affected)

ʃDifferences between sexes in two age groups

Linear enamelhypoplasia

Cribra orbitalia

ʃFactorial analysis of mixed data and

hierarchical clustering (n=62)

ʃVariables

ʃCribra orbitalia (CO)

ʃPorotic hyperostosis of cranial vault (HP)

ʃLinear enamel hypoplasia (LEH)

ʃStature (binary)

FemaleMaleCOHPLEHsmall stature

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