Racism in Marketing Academia: A Necessary Discussion and Call for Action
Bibliographic record
Abstract
Vulnerability resides in the difference between the availability of material or symbolic resources of subjects or groups and access to the structure of social, economic, and cultural opportunities offered by the State, the market, and society. Brazil is considered one of the most unequal countries globally, having 18 million people living in favelas (urban subnormal agglomerations). The lack of essential public services and precarious socioeconomic, sanitation, and housing conditions, added to both high population density and rates of police brutality, are common characteristics in these territories. During the pandemic, community leaders and organizations worked on several strategic fronts, inspiring this working paper to comprehend narratives of coping with Covid-19 in adverse conditions based on Macromarketing literature on vulnerability and resilience. We found four analytical categories in the research: i) Vulnerable Non-White Women; ii) Cooperation network; iii) Confrontation strategies, and iv) Permanent vulnerability.
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 | 0.047 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.022 | 0.052 |
| Scholarly communication | 0.034 | 0.056 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.034 | 0.031 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".