Decentering the White Gaze of Academic Knowledge Production
Bibliographic record
Abstract
This talk highlights some key concerns with the growing "platformitization" of academic knowledge infrastructures that are controlled by a small number of multinational publishers. These oligarch publishers hold enormous power not only over how and where researchers publish, but also over the governance of universities as public institutions. Recent debates on open access have tended to focus on the visible problems with access (namely paywalls and licensing barriers), but insufficient attention has been given to the hidden and invisible power imbalance and asymmetry between the infrastructure providers and the users. I argue that much of these invisible and hidden elements that govern the current knowledge production system are deeply rooted in colonial practices and on Whiteness. This is why, despite the growing acceptance of open access, racial and other forms of inequities in scholarly production continues to widen. I will provide support to my arguments with case studies, and point to means for collective action for decentering Whiteness in knowledge production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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; both teacher heads agree on what is shown here.
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".