Bibliographic record
Abstract
This autobiographical essay ‘takes cultural studies personally’, drawing on experience, identity and the personal to indicate how and why the author is proponent of and is working on developing a model of cultural studies as social justice praxis despite the constraints academia in general and of the university as an institution in particular. The paper travels roughly from the author’s student and teacher days in Sierra Leone through his graduate student days in Canada to his current role as university teacher in the USA. He selectively concentrates on his experience as a teacher of literature (and African multi-role utilitarianism), education and cultural studies (using one of his cultural studies courses and students’ questions about the utility of cultural studies as example), his shifting and overlapping racial/ethnic identities (African/black) and the politics of identity, and his thoughts on the place of theory in cultural studies and a black approach to theory (black ambivalent elaboration) as contributory factors. While this account acts in its own way as an argument for conceptualizing cultural studies as praxis, the primary focus is more modestly on my own autobiographical account as a specific case. In fact, an autobiographical approach is employed precisely to be specific and in the attempt to avoid the pitfalls of over-generalization and the authority of authenticity.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.045 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".