Bibliographic record
Abstract
The last two years have raised important questions about how we can make the teaching of academic writing more equitable. In fact, the current moment invites us to “learn to unlearn” ways of teaching academic writing that perpetuate inequity. In this reflective article, I draw on decolonial theory and antiracist theory to unwind the ways coloniality has shaped the way that I have taught scientific writing for two decades. This work begins with a discussion of the idea of learning to unlearn from decolonial theory. I then examine how that perspective can change the way we teach scientific communication—for example, in contextualizing the development of scientific knowledge as a series of epistemological developments and exchanges, rather than from a zero point of Western thought. Spiraling outward from the classroom, I reflect on how scientific writing is part of a larger matrix of institutional structures that unwittingly compound colonial legacies inequities. In the end, if we are to address inequity in the teaching and assessment of academic writing in new ways, then we need to acknowledge and challenge the legacies of coloniality in the teaching and assessment of academic writing.
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 | 0.043 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.048 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".