A Critical Examination of Race and Antiracism in the Sport for Development Field: An Introduction
Bibliographic record
Abstract
We welcome you to this special issue of the Sociology of Sport Journal which was envisioned as an opportunity to reimagine and recreate sporting spaces for true racial inclusion and equity.We developed the special issue because of what we saw as an absence of a specific and sustained focus on race and racism in Sport for Development (SfD) as well as an antiracist approach to research and analysis of SfD.This was driven, in part, by the ways in which race; racism; antiracism; and diversity, equity, and inclusion (DEI) permeated U.S. society following the murder of George Floyd on May 25, 2020. 1 As statements of solidarity were released, equity audits performed, diversity statements crafted, chief diversity officers hired, DEI training mandated, and strategic plans reframed, we found ourselves (as individuals actively engaged in the SfD field) asking about the ways the field was responding in this moment, given its intentional focus on the use of sport to achieve social change outcomes.As a microcosm of the broader society in which these interventions exist, the field of SfD has a complicated history with race and racism.For example, historical racial power dynamics are apparent in SfD practice, with practitioners (especially those in leadership roles) and funders more often being economically privileged White people, and participants more often being economically oppressed Black, Indigenous, and people of color (BIPOC; Anderson et al., 2021).With this in mind, how did conversations unfold in these settings related to the Black Lives Matter movement, which began in 2013 as a response to the tragic murder of Trayvon Martin, a 17-year-old Black male? 2 How did SfD organizations, particularly those in the United States, respond when a 46-year-old Black man cried out to his mother just before he was killed at the hand and knee of Minneapolis police officers?And how did researchers study these phenomena?Did they identify connections between structural conditions that preceded and contributed to the manifestation of these heinous events?We know the COVID-19 pandemic created space for communities to pause, focus, listen, and learn of the mobilizing message that galvanized a global movement.Black Lives Matter protests emerged across the world in places such as the United States, the Netherlands, France, England, Tokyo, Berlin, Poland, Colombia, Brazil, and Stockholm (Erdekian, 2020).In these moments, Patrisse Cullors, Alicia Garza, and Opal Tometi shared how they worked outside of a system that empowers White supremacy and reproduces systemic marginalization.Their messaging took hold as 2020 saw an increased number of protests, rallies, and demonstrations (Chotiner, 2020).And yet, how did stakeholders across the SfD field respond?The Black Lives Matter movement was not the only racecentric movement focused on advancing justice, equity, and selfdetermination in society and sport in recent years.In 2018, for example, Crystal Echo Hawk launched the IllumiNative organization to "disrupt the invisibility of Native peoples, re-educate Americans, and mobilize public support for key Native issues" (IllumiNative, 2023).This organization produced multiple resources to educate and support an accurate depiction of Indigenous people.In addition, they curated materials to support BIPOC more broadly and to build alliances with non-Indigenous people.These critical, yet thoughtful resources focused on representation (e.g., What's In and What's Out for Native Representation), advocacy (e.g., For Our Future: An Advocate's Guide to Celebrating Indigenous Peoples Day), and mascots (e.g., 2020 Native Mascots Fact Sheet, Unpacking the Mascot Debate Explainer) (IllumiNative, n.d.).The work of IllumiNative sought to promote community, connectivity, and, when necessary, social movement activation.And yet, what was the role and response of SfD to this race-centric movement?Conversely, how did or does SfD address the fact that White supremacy and social control are woven into the fabric of our society?Recent examples within the United States include the efforts of politicians at the local, regional, and national levels to legislate against the use of critical race theory (CRT) and DEI, to ensure that any efforts to share history and facts regarding the experiences of BIPOC are not permitted within educational, corporate, and/or social spaces (Schwartz, 2021(Schwartz, , 2023)).In higher education settings in the United States, at the time of this special issue's publication, 22 bills in 13 states had been introduced that would prohibit colleges from engaging in DEI efforts (Hu et al., 2023).Within the K-12 environment, most recently, the South Carolina House passed legislation that bans "race-based" discussion in K-12 school classrooms and allows parents to sue districts that do not abide by the prospective law (Budds, 2023).Globally, the prevalence of Eurocentric educational curriculum (i.e., perceived primacy of European universities) including the prevailing usage of languages of former colonizing countries (English, French, Spanish, and German) in international communications and concurrent suppression and erasure of Indigenous, African, and Asian epistemologies also reflects the ongoing vicissitudes of settler colonialism (Chen & Mason, 2019).In this special issue, race and racism are the central foci and are conceptualized as socially constructed systems of oppression Cooper
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.029 | 0.039 |
| Scholarly communication | 0.022 | 0.028 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".