Sense-Making Journal: Gender Expression

Journal Index

• Sense-Making Journal: Gender Expression
  • From objectives to the hypothesis
    • Brainstorming
    • Project Goals
    • Potential improvements
  • From hypothesis to data
    • Tools selection
    • Tool usage documentation
  • Data capture
    • Data summary
  • Data insights
    • Tips

From objectives to the hypothesis

Brainstorming

Project Goals

Objective:
I want to have a gender safe environment in Barcelona.

Question:
Are people at IAAC aware of gender differentiating pronouns?

Hypothesis:
People at IAAC are not aware of gender differentiating pronouns.

Potential improvements

• We had difficulty in deciding on the mechanical and material design of the physical interaction.
• We had difficulty in assigning roles to group members.
• We wish we solicited more feedback regarding our questions and hypotheses.
• We should have listened more to our group and try not to impose our opinion.
• This was our first time working together and so we did not know our individual strengths for assigning roles.
• We took more time to fabricate the prototype than necessary and spent less time than anticipated collecting data.

From hypothesis to data

Tools selection

Physical Intervention:

We wanted to make people think of their awareness about gender expression and answer some questions regarding gender pronouns.

This happened in a personal space (bathroom), so people feel confortable about their answers. The interaction was anonymous.

Next time, it would be interesting to put a camera in the interaction space to see people´s behavior when reading the questions and answering.

Tool usage documentation

The 3D board that we fabricated represents a three variable graphic that can be installed in any choosen space.

Data capturing strategy

People were asked 3 different questions:

- Q1: How often people ask you your gender pronouns?
- Q2: How often do you ask people their gender pronouns?
- Q3: What is your personal awareness about gender pronouns?

We designed a 3D board for getting the people’s answers. With a skewer stick each person participating chose the location (question 1, question 2) and then pierced the wood in the board (question 3).

We left this board with the instructions of use in the bathroom of IAAC. The time that we spend collecting data was between Thursday 3:30 pm to Friday 10:30 am.

During this time we got 54 responses.

Materials needed

• Expanded polystyrene foam (80cm x 50cm)
• Wood sticks (skewers)
• Cardboard
• Masking tape
• Markers (green, black, red, purple)
• Ruler
• Packing tape
• Box cutter

Detail setup instructions

1. Brainstorming

3. Definition (questionary, materials, space)

4. Fabrication

5. Instalation

7. Data Collection

9. Data Analysis

11. Report and Conclusions

Data collected

Tips

• Rules for answering questions where not clear for everyone (language and simplicity)
• The bathroom is a safe space but we don´t see interaction.
• If we have too much answers the analog tabulation of data will take too long. We make all the process manually.

Data capture

Data summary

Data insights

Data cleaning/normalization was done in google sheets.

1. Data was entered into the spreadsheet, taking care not to enter the same data twice.
2. Calculations were performed to normalize the data from the arbitrary ranges of each axis to a range of values from 0 to 1.
   • X values were divided by 47 (Sticks were placed in a 47cm long area).
   • Y values were divided by 31 (Sticks were placed in a 31cm wide area).
   • Z values were subtracted by 2 and divided by 15, as well as inverted (Sticks were unable to be inserted further than 2cm, and could be left as much as 15cm without falling out of the foam. Since we were measuring distance, the values made more sense inverted for our purposes).
3. The normalized data was used in the next stage, analysis:

Tips

• Sometimes it’s useful to have a primary variable against which you’d like to compare all the others and see their correlations. When thinking of the variable we never considered that.
• The data collection method can have some problems, misunderstanding from users, etc. Therefore, when finding insights, sometimes it’s hard to see whether those weird insights are because of the mistake in data collection process or if they are real.
• Another thing is that our Z variable (awareness) is highly subjective in user’s mind, therefore, the insights might not be representative.
• Not having enough data points is definitely an issue sometimes.