Against mastery: Epistemic decolonizing in the margins of the Business School
Bibliographic record
Abstract
In this provocation, I argue that epistemic decolonizing is an opposition to the Master promulgator of knowledge, the Western/Eurocentric epistemic subject position. This epistemic refusal of mastery can only happen in the margins of the Business School, and as such, it is always an unfinished project whose incompleteness should be celebrated. To develop my argument, I proceed in three steps. First, I conceptualize the Business School as a postcolony , that is, a realm of extended epistemic domination rooted in the institution’s colonial historical role. Second, I suggest an alternative understanding of the margins not only rooted in spatiality, location, or identity but as a specific minoritarian epistemic position against mastery within the postcolony. These margins are not stable and immutable but relational , constantly being made, re-made, transformed, and negotiated. They are the location for an affirmative, generative and imaginative ongoing sabotage of epistemic domination. Finally, I offer that epistemic decolonizing as a minoritarian engagement, is unavoidably incomplete, unfinished, and unfinishable as knowledge always already exists and is always already weaved from a multiplicity of entangled historical, cultural, political, and disciplinary threads.
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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.064 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".