Decolonizing marketing
Bibliographic record
Abstract
In January 2021, the ETHOS Research Center at Bayes Business School, along with the CRIS Research Center at Royal Holloway University of London, hosted an event entitled Decolonizing the Business School. Over 500 attendees participated, from all business disciplines, testifying to the strong levels of interest in this topic. Marketing was particularly active, with over 100 participants. In this article, I (Giana Eckhardt, one of the organizers of the event) speak with the marketing break out room facilitators – Russ Belk, Tonya Bradford, Susan Dobscha, Güliz Ger and Rohit Varman – in a wide-ranging conversation about what decolonization means to the field of marketing, and what marketing academics can do if they would like to explore these ideas further. First, we offer a brief introduction to decolonization. Also, a list of resources for the interested reader is presented as well as ideas for further exploration in this nascent domain at the end.
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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.037 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".