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WHRDs are self-identified women and lesbian, bisexual, transgender, queer and intersex (LBTQI) people and others who defend rights and are subject to gender-specific risks and threats due to their human rights work and/or as a direct consequence of their gender identity or sexual orientation.
WHRDs are subject to systematic violence and discrimination due to their identities and unyielding struggles for rights, equality and justice.
The WHRD Program collaborates with international and regional partners as well as the AWID membership to raise awareness about these risks and threats, advocate for feminist and holistic measures of protection and safety, and actively promote a culture of self-care and collective well being in our movements.
WHRDs are exposed to the same types of risks that all other defenders who defend human rights, communities, and the environment face. However, they are also exposed to gender-based violence and gender-specific risks because they challenge existing gender norms within their communities and societies.
We work collaboratively with international and regional networks and our membership
We aim to contribute to a safer world for WHRDs, their families and communities. We believe that action for rights and justice should not put WHRDs at risk; it should be appreciated and celebrated.
Promoting collaboration and coordination among human rights and women’s rights organizations at the international level to strengthen responses concerning safety and wellbeing of WHRDs.
Supporting regional networks of WHRDs and their organizations, such as the Mesoamerican Initiative for WHRDs and the WHRD Middle East and North Africa Coalition, in promoting and strengthening collective action for protection - emphasizing the establishment of solidarity and protection networks, the promotion of self-care, and advocacy and mobilization for the safety of WHRDs;
Increasing the visibility and recognition of WHRDs and their struggles, as well as the risks that they encounter by documenting the attacks that they face, and researching, producing, and disseminating information on their struggles, strategies, and challenges:
Mobilizing urgent responses of international solidarity for WHRDs at risk through our international and regional networks, and our active membership.
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Este año honramos a 19 defensoras de la región de América Latina y el Caribe. De ellas, 16 fueron asesinadas, incluyendo a 6 periodistas y 4 defensoras LGBTQI. Únete a nosotras en la conmemoración de sus vidas y trabajo, compartiendo los memes aquí incluidos con tus colegas, amistades y redes; y tuiteando las etiquetas #WHRDTribute y #16Días.
Por favor, haz click en cada imagen de abajo para ver una versión más grande y para descargar como un archivo.












NOUS SOMMES LA SOLUTION
We are the Solution
Oui, l’enquête est accessible depuis les téléphones intelligents.
7 Women Human Rights Defenders from across the South and Southeast Asian region are honored in this year’s Online Tribute. These defenders have made key contributions to advancing human and women’s rights, indigenous people’s rights, and the right to education. These WHRDs were lawyers, women’s rights activists, scholars, and politicians. Please join AWID in commemorating t their work and legacy by sharing the memes below with your colleagues, networks and friends and by using the hashtags #WHRDTribute and #16Days.
Please click on each image below to see a larger version and download as a file







This is Mariama Sonko, an inspiring small-scale rural farmer, eco-feminist and a woman human rights defender.
She lives in Niaguiss, a town in the southwest of Senegal. Growing up in a family and community of rural farmers, she witnessed the essential role of women in food production and seed preservation from a very early age, while also being immersed in the rhythms and working of the land. Mariama has been defending local agricultural knowledge and peasant practices since the 1990s. As a mother of five children, the food she grows herself is the main source of sustenance for her family.
She is currently the president of “Nous Sommes la Solution'' and is involved in promoting agroecological practices and family farming, encouraging food sovereignty, biodiversity and farmer seed preservation, and demanding equitable access to resources and land for women across West Africa.
Source: AWID’s Feminist Realities Festival Crear | Résister | Transform - Day 2/ 2ème jour/ 2º día
As the WITM survey is focused on resourcing realities for feminist organizations, most questions ask about your group’s funding between 2021–2023. You will need to have this information with you to fill out the survey (e.g., your annual budgets and key sources of funding).
Before starting the WITM research methodology, it is important you prepare the background and know what to expect.
With AWID’s WITM research methodology, we recommend that you first review the entire toolkit.
While this toolkit is designed to democratize WITM research, there are capacity constraints related to resources and research experience that may affect your organization’s ability use this methodology.
Use the “Ready to Go?” Worksheet to assess your readiness to begin your own WITM research. The more questions you can answer on this worksheet, the more prepared you are to undertake your research.
Before beginning any research, we recommend that you assess your organization’s connections and trust within your community.
In many contexts, organizations may be hesitant to openly share financial data with others for reasons ranging from concerns about how the information will be used, to fear of funding competition and anxiety over increasing government restrictions on civil society organizations.
As you build relationships and conduct soft outreach in the lead-up to launching your research, ensuring that your objectives are clear will be useful in creating trust. Transparency will allow participants to understand why you are collecting the data and how it will benefit the entire community.
We highly recommend that you ensure data is collected confidentially and shared anonymously. By doing so, participants will be more comfortable sharing sensitive information with you.
We also recommend referring to our “Ready to Go?” Worksheet to assess your own progress.

Sí, es absolutamente confidencial. Tus respuestas se borrarán al término del procesamiento y el análisis de los datos, y se utilizarán únicamente a los fines de la investigación. Los datos NUNCA se compartirán fuera de AWID y solo los procesarán el personal y consultorxs de AWID abocadxs al proyecto ¿Dónde está el dinero?
Damos prioridad a tu privacidad y anonimato. Los detalles de nuestra política de privacidad se encuentran disponibles aquí.
We all can dance
by Mechthild Möhring (aka serialmel)
How I punt myself at the narrow hard knitting I once retrieved. I'm dancing in the kitchen when I'm alone. Gracile and powerful. When I'm in company I'm clumsy. My body scandalizes, scandalizes the laws of look I feel, scandalizes the words which banished me. "Of course she can dance, it's in her blood as a Black person." "If she is able to dance nicely she is good in bed" they whisper, they murmur, no - they say it openly into my face. They smirk and rub themselves against me and let me move back. I stumble and fall. My feet reject their duty. Bearish I get out of breath. Smiling I place myself out of events and notice how my face freezes into a mask.
Translated into English by Tsepo Bollwinkel
Original in German
Tanzen können wir alle
Von Mechthild Möhring (aka serialmel)
Wie ich mich stosse an den engen, harten Maschen, in die ich mich einst zurückgezogen habe. Ich tanze in der Küche, wenn ich allein bin. Grazil und kraftvoll. Wenn ich in Gesellschaft bin, bin ich unbeholfen. Mein Körper eckt an, an die Gesetze des Blicks, den ich spüre, an die Worte, die mich bannten. „Natürlich kann sie tanzen, als Schwarze hat sie das im Blut.“ „Wenn sie gut tanzen kann, dann ist sie auch gut im Bett“ flüstern sie, raunen sie, nein, sie sagen es mir laut ins Gesicht. Sie grinsen und reiben sich an mir und lassen mich zurückweichen. Ich stolpere und falle. Meine Füsse verweigern ihren Dienst. Tollpatschig gerate ich ausser Atem. Lächelnd setze ich mich an den Rand des Geschehens und bemerke, wie mein Gesicht zur Maske erstarrt.
This section will guide you on how to ensure your research findings are representative and reliable.
In this section:
- Collect your data
1. Before launch
2. Launch
3. During launch- Prepare your data for analysis
1. Clean your data
2. Code open-ended responses
3. Remove unecessary data
4. Make it safe- Create your topline report
- Analyze your data
1. Statistical programs
2. Suggested points for analysis
If you also plan to collect data from applications sent to grant-making institutions, this is a good time to reach out them.
When collecting this data, consider what type of applications you would like to review. Your research framing will guide you in determining this.
Also, it may be unnecessary to see every application sent to the organization – instead, it will be more useful and efficient to review only eligible applications (regardless of whether they were funded).
You can also ask grant-making institutions to share their data with you.
Your survey has closed and now you have all this information! Now you need to ensure your data is as accurate as possible.
Depending on your sample size and amount of completed surveys, this step can be lengthy. Tapping into a strong pool of detail-oriented staff will speed up the process and ensure greater accuracy at this stage.
Also, along with your surveys, you may have collected data from applications sent to grant-making institutions. Use these same steps to sort that data as well. Do not get discouraged if you cannot compare the two data sets! Funders collect different information from what you collected in the surveys. In your final research report and products, you can analyze and present the datasets (survey versus grant-making institution data) separately.
There are two styles of open-ended responses that require coding.
Questions with open-ended responses
For these questions, you will need to code responses in order to track trends.
Some challenges you will face with this is:
If using more than one staff member to review and code, you will need to ensure consistency of coding. Thus, this is why we recommend limiting your open-ended questions and as specific as possible for open-ended questions you do ask.
For example, if you had the open-ended question “What specific challenges did you face in fundraising this year?” and some common responses cite “lack of staff,” or “economic recession,” you will need to code each of those responses so you can analyze how many participants are responding in a similar way.
For closed-end questions
If you provided the participant with the option of elaborating on their response, you will also need to “up-code” these responses.
For several questions in the survey, you may have offered the option of selecting the category “Other” With “Other” options, it is common to offer a field in which the participant can elaborate.
You will need to “up-code” such responses by either:
Analyze the frequency of the results
For each quantitative question, you can decide whether you should remove the top or bottom 5% or 1% to prevent outliers* from skewing your results. You can also address the skewing effect of outliers by using median average rather than the mean average. Calculate the median by sorting responses in order, and selecting the number in the middle. However, keep in mind that you may still find outlier data useful. It will give you an idea of the range and diversity of your survey participants and you may want to do case studies on the outliers.
* An outlier is a data point that is much bigger or much smaller than the majority of data points. For example, imagine you live in a middle-class neighborhood with one billionaire. You decide that you want to learn what the range of income is for middle-class families in your neighborhood. In order to do so, you must remove the billionaire income from your dataset, as it is an outlier. Otherwise, your mean middle-class income will seem much higher than it really is.
Remove the entire survey for participants who do not fit your target population. Generally you can recognize this by the organizations’ names or through their responses to qualitative questions.
To ensure confidentiality of the information shared by respondents, at this stage you can replace organization names with a new set of ID numbers and save the coding, matching names with IDs in a separate file.
With your team, determine how the coding file and data should be stored and protected.
For example, will all data be stored on a password-protected computer or server that only the research team can access?
A topline report will list every question that was asked in your survey, with the response percentages listed under each question. This presents the collective results of all individual responses.
Tips:
- Consistency is important: the same rules should be applied to every outlier when determining if it should stay or be removed from the dataset.
- For all open (“other”) responses that are up-coded, ensure the coding matches. Appoint a dedicated point person to randomly check codes for consistency and reliability and recode if necessary.
- If possible, try to ensure that you can work at least in a team of two, so that there is always someone to check your work.
Now that your data is clean and sorted, what does it all mean? This is the fun part where you begin to analyze for trends.
Are there prominent types of funders (government versus corporate)? Are there regions that receive more funding? Your data will reveal some interesting information.
Smaller samples (under 150 responses) may be done in-house using an Excel spreadsheet.
Larger samples (above 150 responses) may be done in-house using Excel if your analysis will be limited to tallying overall responses, simple averages or other simple analysis.
If you plan to do more advanced analysis, such as multivariate analysis, then we recommend using statistical software such as SPSS, Stata or R.
NOTE: SPSS and Stata are expensive whereas R is free.
All three types of software require staff knowledge and are not easy to learn quickly.
Try searching for interns or temporary staff from local universities. Many students must learn statistical analysis as part of their coursework and may have free access to SPSS or Stata software through their university. They may also be knowledgeable in R, which is free to download and use.

• 2 - 3 months
• 1 or more research person(s)
• Translator(s), if offering survey in multiple languages
• 1 or more person(s) to assist with publicizing survey to target population
• 1 or more data analysis person(s)
• List of desired advisors: organizations, donors, and activists
• Optional: an incentive prize to persuade people to complete your survey
• Optional: an incentive for your advisors
Survey platforms:
• Survey Monkey
• Survey Gizmo (Converts to SPSS for analysis very easily)
Examples:
• 2011 WITM Global Survey
• Sample of WITM Global Survey
• Sample letter to grantmakers requesting access to databases
Visualising Information for Advocacy:
• Cleaning Data Tools
• Tools to present your data in compelling ways
• Tutorial: Gentle Introduction to Cleaning Data