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Affiliate Data Survey Report 2021

“ Cultures and climates differ all over the world, but people are the same. They will gather in public if you give them a good place to do it. ”

— Jan Gehl, (PPS. 2008c)[1]


Wikimedia Affiliates have become invaluable as public spaces for community members to meet in person and to continue that meeting online. This year, 755 community members from 80 countries started the survey while 638 completed the survey. Affiliate primary contacts worked together with evaluators to implement a randomised sampling of their members in accordance with the designed sampling frame. This experimental participatory sampling strategy yielded a 67% year on year increase in membership participation. Most of these members have been with their affiliate between 3-5 years (39% User Groups and 26% Chapters/ThOrg), and belong to one affiliate (42% User Groups and 59% Chapters/ThOrg). Responses have been weighted to the affiliate they represent so that all affiliates are counted equally in the data summaries that follow.

Demographics

Affiliates strive to provide welcoming public spaces for a diverse population of the community. However, the overall demographic profile of polled affiliate members who responded to the survey suggests that there is room for improvement, to get affiliate membership to reflect the world. Even so, the following insights were observed:

  • Most affiliate members were between the 18 - 44 years age group (90% User Groups and 79% chapters/ThOrg).
  • Significantly more women members (⬆11%) participated in this survey (37% User Groups and 27% Chapters/ThOrg). 5% more people opted not to disclose their gender when compared to 2020 participants.
  • Most affiliate members have a University degree (32% for both User Groups and Chapters/ThOrg) or masters degree (28% for both User Groups and Chapters/ThOrg).[note 1]
  • A quarter of the affiliate members are financially insecure (27% User Groups and 21% Chapters/ThOrg) and may be making relatively significant investments to participate in the movement.
  • Most affiliate members who identified as minority felt discriminated against on the basis of their ethnic group.
  • Very few members responding to the survey reported living with visual impairment and hearing loss; this differs significantly from world averages (Wagner, L. 2021, May 28).

Social climate

Affiliate members were asked to answer a set of questions about specific experiences in their community spaces, to better understand their perceptions about their social environment in the Wikimedia ecosystem. Our 2021 set of social climate measures included a few factors looking at multiple items to understand:

  • Do community members experience an environment that supports free and open expression of ideas among members of different backgrounds? (Inclusive Interactions)
  • To what extent are members aware of their motivations (Awareness of Self) and that of others? (Awareness of Others)
  • How do members feel about their affiliate’s ability to support them when a dispute or conflict occurs among members? (Affiliate conflict support)

In addition, we explore additional aspects including:

  • How much do members feel that others are interested in building successful cooperative relationships? (Collaborative Intention)
  • How much do community members identify with, are inspired by, and promote being a part of the Wikimedia movement and its projects? (Engagement)
  • How respected do people feel as a part of the Wikimedia movement, its organizations, and decision-making processes? (Feelings of Belonging)
  • How much do community members feel that others seek fair solutions and are willing to talk through competing personal interests? (Problem Solving & Negotiating)
  • How committed are members to diversity as individuals? (Attitudes towards value of diversity)
  • How members perceive the committed affiliate leaders to diversity? (Commitment to diversity)

While affiliate members continue to express satisfaction about the social climate that is created by affiliates, they had a significant doubt about their affiliate’s to commitment diversity.[note 2]

Programmatic areas of focus

Affiliate communities continue to lead organizing/outreach work that grows the Wikimedia movement through programmatic activities which support communities to engage new audiences and bring high quality contributors and content to our projects.

Affiliate members continue to focus on GLAM, Education and Photography programs, which are traditional staple programs for all affiliate types, while Gender (53% User Groups and 46% Chapters/ ThOrg) and Medicine & Health programs (30% User Groups and 29% Chapters/ ThOrg) continue to be rated low on the focus spectrum. There is clearly a need for consistent and widespread initiatives to support the programmatic focus reported by affiliate members. There were no statistically significant changes in neither the means nor the distributions in a year-on-year overall analysis of programmatic focus.

Awareness of resources

Affiliate members were more aware of grants and funding from Wikimedia Foundation than grants from other Wikimedia affiliates or non-Wikimedia organizations. However, they were quite aware of both. Importantly, this survey was conducted before the grant refresh and does not apply to the new grant programs.

Compared to those polled in 2020, 22% more affiliate members were aware of affiliate-to-affiliate grants, while 31% more were aware of any funding resources. However, there was a slight improvement (⬆10%) in the awareness of grants or other funding from non-Wikimedia organizations. This lack of awareness of alternative funding sources continues to present a sustainability risk for the movement, as it limits the affiliate’s ability to diversify its funding sources.

There were no statistically significant changes in either the means or the distributions in a year-on-year overall analysis of awareness of resources amongst affiliate members.

Contributor motivations

Affiliate members were asked to answer a set of questions about their motivations for contributing to Wikimedia projects.

The majority of members contribute to Wikimedia projects for altruistic reasons, including volunteering to engage people in shared knowledge (53% User Groups and 46% Chapters/ ThOrg) and contributing directly to open knowledge (53% User Groups and 46% Chapters/ ThOrg).

Interestingly, almost two-thirds of the members cited that they were contributing to fill content gaps in the Wikimedia projects (suggesting some propensity towards being involved in organizing/leading programs), while just above half contributed to being able to engage and share opinions with other Wikimedians. Additional research is required to understand secondary motivations, such as the need to be part of a community or the need for recognition and fulfillment.

So what?

To improve the quality of membership experiences in affiliate spaces
  • AffCom should encourage affiliates to strengthen their commitment to diversity by intentionally recruiting members of diverse demographics.
  • The Wikimedia Foundation should allocate more resources to create diverse and strategic content programs to meet affiliate programmatic focus.

Notes

  1. ↑ When comparing education levels self-reported by affiliate members, an Independent-Samples Mann-Whitney U test found a statistically significant change both in medians (χ2(1) =125.261, p =0.000) and distributions (χ2(1) =142960.00, p =0.000). Demonstrating an increase in mean rank in 2021 (591.58), compared to 2020 (mean rank = 181.60).
  2. ↑ When comparing overall rating of social climate factors self-reported by affiliate members between 2020 and 2021, an Independent-Samples Mann-Whitney U test found the following about the medians and distributions:
    a. Change in Collaborative Intention were seen in distribution (χ2(1) = 12636.00, p = 0.025) while a
    change in medians could not be calculated. Demonstrating a decrease in mean rank in 2021 (189.77),
    compared to 2020 (mean rank = 206.99).
    b. Change in Self-Awareness was seen in distribution (χ2(1) = 18938.50, p = 0.000) while a
    change in medians could not be calculated. Demonstrating an increase in mean rank in 2021 (207.62),
    compared to 2020 (mean rank = 98.19).
    c. Change in Problem Solving was seen in distribution (χ2(1) = 15904.00, p = 0.000) while a
    change in medians could not be calculated. Mean ranks were not output (i.e., could not be calculated).
    d. Change in Engagement was seen in distribution (χ2(1) = 14782.50, p = 0.001) while a
    change in medians could not be calculated. Demonstrating an increased mean rank in 2021 (196.15),
    compared to 2020 (mean rank = 177.05).
    e. Change in Feelings of Belonging was seen in distribution (χ2(1) = 11744.50, p = 0.005) while a
    change in medians could not be calculated. Demonstrating a decreased mean rank in 2021 (184.92),
    compared to 2020 (mean rank =209.56).
    f. Change in Affiliate Conflict Support was seen in distribution (χ2(1) = 11186.00, p = 0.000) while a
    change in medians could not be calculated. Demonstrating a decreased mean rank in 2021 (184.44),
    compared to 2020 (mean rank = 215.91).
    g. Change in Inclusive Interactions was seen in distribution (χ2(1) = 15503.00, p = 0.012) while a
    change in medians could not be calculated. Demonstrating an increased mean rank in 2021 (200.55),
    compared to 2020 (mean rank = 177.57).
    h. Change in Individuals Commitment to Diversity was seen in medians (χ2(1) = 80.161, p = 0.000) in distribution
    (χ2(1) = 5884.50, p = 0.000). Demonstrating a decreased mean rank in 2021 (161.52), compared to 2020
    (mean rank =262.54).
    i. Change in Valuing of Diversity was seen in distribution (χ2(1) = 47799, p = 0.001) while a change in medians could not
    be calculated. Demonstrating a decreased mean rank in 2021 (175.73), compared to 2020 (mean rank = 204.26).

References

  1. ↑ PPS. (2008c, December 31). Jan Gehl. The Wild World of Placemaking. Project for Public Spaces. https://www.pps.org/article/the-wild-world-of-placemaking

Statistical References:

  • Dunn, O. J. (1964). Multiple comparisons using rank sums. Technometrics, 6, 241-252.
  • Kruskal, W. H., & Wallis, W. A. (1952). Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association, 47(260), 583-621.
  • Laerd Statistics (2015). Statistical tutorials and software guides. Retrieved from https://statistics.laerd.com
  • Lehmann, E. L. (2006). Nonparametrics: Statistical methods based on ranks. New York: Springer.
  • Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two-random variables is stochastically larger than the other. The Annals of Mathematical Statistics, 18(1), 50-60.
  • Sheskin, D. J. (2011). Handbook of parametric and nonparametric statistical procedures (5th ed.). Boca Raton, FL: Chapman & Hall/CRC Press.
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