
Dr. Nuzhat Amin

Lxs defensorxs se identifican a sí mismas como mujeres y personas lesbianas, bisexuales, transgénero, queer e intersex (LBTQI) y otrxs que defienden derechos y que debido a su trabajo en derechos humanos están bajo riesgos y amenazas específicos por su género y/o como consecuencia directa de su identidad de género u orientación sexual.
Lxs defensorxs son objeto de violencia y discriminación sistemáticas debido a sus identidades y su inclaudicable lucha por derechos, igualdad y justicia.
El Programa Defensorxs colabora con contrapartes internacionales y regionales así como con lxs afiliadxs de AWID para crear conciencia acerca de estos riesgos y amenazas, abogar por medidas de protección y de seguridad que sean feministas e integrales, y promover activamente una cultura del autocuidado y el bienestar colectivo en nuestros movimientos.
lxs defensorxs enfrentan los mismos tipos de riesgos que todxs lxs demás defensorxs de derechos humanos, de comunidades y del medio ambiente. Sin embargo, también están expuestas a violencia y a riesgos específicos por su género porque desafían las normas de género de sus comunidades y sociedades.
Nos proponemos contribuir a un mundo más seguro para lxs defensorxs, sus familias y comunidades. Creemos que actuar por los derechos y la justicia no debe poner en riesgo a lxs defensorxs, sino que debe ser valorado y celebrado.
Promoviendo la colaboración y coordinación entre organizaciones de derechos humanos y organizaciones de derechos de las mujeres en el plano internacional para fortalecer la capacidad de respuesta en relación a la seguridad y el bienestar de lxs defensorxs.
Apoyando a las redes regionales de defensorxs y de sus organizaciones, tales como la Iniciativa Mesoamericana de Mujeres Defensorxs de Derechos Humanos y la WHRD Middle East and North Africa Coalition [Coalición de Defensorxs de Derechos Humanos de Medio Oriente y África del Norte], promoviendo y fortaleciendo la acción colectiva para la protección, poniendo el énfasis en establecer redes de solidaridad y protección, promover el autocuidado y la incidencia y movilización por la seguridad de lxs defensorxs.
Aumentando la visibilidad y el reconocimiento de lxs defensorxs y sus luchas, así como de los riesgos que enfrentan, a través de la documentación de los ataques que sufren, e investigando, produciendo y difundiendo información sobre sus luchas, estrategias y desafíos.
Movilizando respuestas urgentes de solidaridad internacional para lxs defensorxs que están en riesgo a través de nuestras redes internacionales y regionales y de nuestrxs afiliadxs activxs.
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.
Écoutez cette histoire :
✉️ Inscripción presencial cerrada. Regístrate para la transmisión en vivo aquí
Evento en inglés
📅 Miércoles 12 de marzo de 2025
🕒 De 12:00 a 01:30 pm, EST
🏢 PNUD, 304 E 45th St. Doha Room, 11th Floor (FF Building)
Organizan: PNUD, Femena, SRI y AWID
Nous mettrons le site à jour avec les informations pertinantes le moment venu.
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
Vous N’AVEZ PAS besoin d’un visa pour assister au Forum à Taipei si vous détenez un passeport émis par l’un des pays suivants (la durée de séjour autorisée varie d’un pays à l’autre) :
Allemagne, Andorre, Australie, Autriche, Belgique, Belize, Bulgarie, Brunei, Canada, Chili, Chypre, Croatie, Danemark, Espagne, Estonie, Eswatini, État de la Cité du Vatican, États-Unis, Finlande, France, Grèce, Guatemala, Haïti, Honduras, Hongrie, Îles Marshall, Islande, Irlande, Israël, Italie, Japon*, Lettonie, Liechtenstein, Lituanie, Luxembourg, Malaisie, Malte, Monaco, Nauru, Nicaragua, Norvège, Nouvelle-Zélande, Palaos, Paraguay, Pays-Bas, Philippines, Pologne, Portugal, République de Corée, République dominicaine, République tchèque, Roumanie, Royaume-Uni, Russie, Saint-Christophe-et-Niévès, Sainte-Lucie, Saint-Marin, Saint-Vincent-et-les-Grenadines, Singapour, Slovaquie, Slovénie, Suède, Suisse, Tuvalu.
Les personnes détentrices de tout autre passeport AURONT BESOIN D’UN VISA pour se rendre à Taipei.
Remarque :
Une fois inscrit·e au Forum, il est probable que vous receviez un code relatif à l’événement vous permettant de demander un visa en ligne, quelle que soit votre nationalité.
Nous vous donnerons plus d’informations à ce sujet après l’ouverture du processus d’inscription.
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.
Escucha esta historia aquí:
If the positions advertised have closed, or just don’t match your area of interest and expertise, we suggest the following resources for your job search.
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
une identité de genre latino-américaine
Le terme travesti désigne une identité de genre latino-américaine sans équivalent dans d'autres langues, et exclusivement féminine. C'est une personne désignée de genre masculin à la naissance qui s'identifie comme une femme.
Elles doivent toujours être adressées avec le pronom “elle”. Travesti n'est pas seulement une identité de genre située en dehors de la binarité de genre, c'est aussi une identité culturelle enracinée dans les mouvements latino-américains.
Le terme était initialement péjoratif, mais a ensuite été réapproprié comme symbole de résistance et de dignité. Chaque travesti est trans parce qu'elle ne s'identifie pas au genre désigné à la naissance, mais toutes les travesti ne se considèrent pas comme femmes trans, car travesti est déjà une identité de genre en soi.
Référence: Berkins, Lohana. (2006). Travestis: una Identidad Política . Trabajo presentado en el Panel Sexualidades contemporáneas en las VIII Jornadas Nacionales de Historia de las Mujeres/ III Congreso Iberoamericano de Estudios de Género Diferencia Desigualdad. Construirnos en la diversidad, Villa Giardino, Córdoba, 25 al 28 de octubre de 2006.
Search for funders based on their requirements for groups to be registered.