Bibliographic record
Abstract
The past few years have seen a rise in attempts to decolonize curricula, pedagogies, classrooms and knowledge production. In this paper, I provided a six-step argument for reading and teaching Weber – among other scholarly writings – in times of intellectual decolonization. I argue that (1) Weber’s calls for scientific rigour and the prevalence of social causality over biological, culturalist or other essentialist interpretations, as well as (2) his uncovering of racist ideology embedded in social institutions are more relevant than ever. I then follow Weber in (3) theorizing the emergence of ethnic feelings of communalization as the outcome of unequal power relations tied to migration, conquest, and colonization. (4) This relationship of domination/subordination is conducive to a differential sense of self and dignity with members of majority and minority populations. (5) Based on Weber’s epistemology, these diverse “standpoints” need to be included into the curriculum as they contribute valuable pieces to the overarching puzzle of human knowledge. (6) Weber’s call for a strict separation of science and politics, however, prohibits political activism and “professorial prophecy” in the lecture hall or classroom. For Weber, the professor should not teach students what they should do, only what they may want to do.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.047 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".