Rescuing marketing from its colonial roots: a decolonial anti-racist agenda
Bibliographic record
Abstract
Purpose This paper illuminates the mechanisms through which marketing practice and institutions produced, normalized and institutionalized systemic racism in support of imperialism, colonization and slavery to provide impetus for transformational change. Critical race research is drawn on to propose paths toward decolonial and anti-racist research agenda and practice. Design/methodology/approach The paper integrates multidisciplinary literature on race, racism, imperialism, colonialism and slavery, connecting these broad themes to the roles marketing practices and institutions played in creating and sustaining racism. Critical race theory, afro pessimism, postcolonial theories, anti-racism and decoloniality provide conceptual foundations for a proposed transformative research agenda. Findings Marketing practices and institutions played active and leading roles in producing, mass mobilizing and honing racist ideology and the imagery to support imperialism, colonial expansion and slavery. Racist inequalities in market systems were produced globally through active collusion by marketing actors and institutions in these historical forces creating White advantage and Black dispossession that persist; indicating an urgent need for transformative anti-racists and decolonial research agendas. Research limitations/implications Covering these significant historical forces inevitably leaves much room for further inquiry. The paper by necessity “Mango picked” the most relevant research, but a full coverage of these topics was beyond the scope of this paper. Practical implications Marketing practitioners found themselves at the epicenter of a crisis during the Black Lives Matter protests. This paper aims to foster anti-racist ad decolonial research to guide practice. Social implications This paper addresses systemic and institutional racism, and marketplace inequalities – urgent societal challenges. Originality/value To the best of the authors’ knowledge, the paper is the first in marketing to integrate multidisciplinary literature on historical forces of imperialism, colonization and slavery to illuminate marketing’s influential role in producing marketplace racism while advancing an anti-racist and de-colonial research agenda.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.043 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".