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Data visualization as a tool for socioeconomic empowerment and gender equality promotion.

Avatar: Francisco Duque Lima Francisco Duque Lima
23/11/2021 16:20  

Context
- Women are just a third of the workforce in the movie industry;
- Men get more funding than women for film productions;
- There is an established idea that women succeed in making documentaries, which hinders access to opportunities in other genres. Eg: The percentage of women in charge or involved in fiction productions is extremely low. (Almost three times more women directing documentaries than fiction);
- There is only sex-disaggregated data available (it is not gender data, it does not include gender expression and gender identity);
- We don't know about the gender bias across race, sexuality, class, ethnicity, nationality, disability, age, etc.

[Data pertains to the Spanish film industry. Time frame, 2015 to 2020]

Hypothesis
Accessible data narratives have a positive impact in promoting gender equality.
Collaboration
1) Feedback (opinions on the hypothesis, its viability and pitfalls, as well as missing perspectives) 2) Reflections (on the issue as a whole and on their own experiences and biases); 3) Data (insight about the movie audience, forms of data collection, other data resources, concerns with data collection); 4) Commitment (
Related data
Representativity of Women in the Spanish Movie Industry - Gender Data Lab: https://data.genderdatalab.org/dataset/representacion-de-mujeres-en-el-cine-espanol GENDER GAP AT CINEMA INDUSTRY SPAIN - Gender Data Lab: https://data.genderdatalab.org/dataset/gender-gap-at-cinema-industry-spain
Indicators
Women-centered gender gap;
Evaluation
To evaluate community contributions:
Clear, transparent, substantial feedback;
Respectful and inclusive approaches;

Meta-evaluation:
Visual uptake on discussion, awareness and apparent interest;
Noticeable changes in practices or discourse in the industry;

List of Endorsements

Avatar: Francisco Duque Lima Francisco Duque Lima

AUTOMATED DATA CONTENT ANALYSIS

  • Keyword Graph
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Date range:  
Color codes for sentiment analysis: Negative vs Positive
Improve Hypothesis Improve this hypothesis by modifying its title and body
3 Supports
Endorsements count1
Data visualization as a tool for socioeconomic empowerment and gender equality promotion. Comments 2

Reference: IN-PROP-2021-11-157
Version number 1 (of 1) see other versions
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The piece of text below is a shortened, hashed representation of this content. It's useful to ensure the content hasn't been tampered with, as a single modification would result in a totally different value.

Value: e0161b0653a2f1de94d717beaaad0a5cd3d28fa3141320c2c543cc2eddc4c791

Source: {"body":{"en":"<xml><dl class=\"decidim_awesome-custom_fields\" data-generator=\"decidim_awesome\" data-version=\"0.7.2\"><dt name=\"textarea-1635451235315\">Context</dt><dd id=\"textarea-1635451235315\" name=\"textarea\"><div>- Women are just a third of the workforce in the movie industry;\n- Men get more funding than women for film productions;\n- There is an established idea that women succeed in making documentaries, which hinders access to opportunities in other genres. Eg: The percentage of women in charge or involved in fiction productions is extremely low. (Almost three times more women directing documentaries than fiction);\n- There is only sex-disaggregated data available (it is not gender data, it does not include gender expression and gender identity);\n- We don't know about the gender bias across race, sexuality, class, ethnicity, nationality, disability, age, etc.\n\n[Data pertains to the Spanish film industry. Time frame, 2015 to 2020]</div></dd><dt name=\"text-1635451312580\">Hypothesis</dt><dd id=\"text-1635451312580\" name=\"text\"><div>Accessible data narratives have a positive impact in promoting gender equality.</div></dd><dt name=\"text-1635451372562\">Collaboration</dt><dd id=\"text-1635451372562\" name=\"text\"><div>1) Feedback (opinions on the hypothesis, its viability and pitfalls, as well as missing perspectives) 2) Reflections (on the issue as a whole and on their own experiences and biases); 3) Data (insight about the movie audience, forms of data collection, other data resources, concerns with data collection); 4) Commitment (</div></dd><dt name=\"text-1635451427513\">Related data</dt><dd id=\"text-1635451427513\" name=\"text\"><div>Representativity of Women in the Spanish Movie Industry - Gender Data Lab: https://data.genderdatalab.org/dataset/representacion-de-mujeres-en-el-cine-espanol GENDER GAP AT CINEMA INDUSTRY SPAIN - Gender Data Lab: https://data.genderdatalab.org/dataset/gender-gap-at-cinema-industry-spain</div></dd><dt name=\"textarea-1635451479530\">Indicators</dt><dd id=\"textarea-1635451479530\" name=\"textarea\"><div>Women-centered gender gap; \n</div></dd><dt name=\"textarea-1635451518445\">Evaluation</dt><dd id=\"textarea-1635451518445\" name=\"textarea\"><div>To evaluate community contributions: \nClear, transparent, substantial feedback;\nRespectful and inclusive approaches;\n\nMeta-evaluation:\nVisual uptake on discussion, awareness and apparent interest;\nNoticeable changes in practices or discourse in the industry; </div></dd></dl></xml>"},"title":{"en":"Data visualization as a tool for socioeconomic empowerment and gender equality promotion."}}

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 870792.
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