Open City, Open Text: Teju Cole, Digital Humanities, and the Limits of Epistemologyand the Limits
Bibliographic record
Abstract
Teju Cole’s artistic practice straddles the border between digital and analog forms. By repurposing each medium’s conventions, he foregrounds the epistemological assumptions that undergird them. His work affords us new insights into how the internet and the Digital Humanities have changed our reading practices, especially as these relate to “African literature.” In Open City, the blurring of generic boundaries between born-digital and print artifacts forces both reader and author to grapple with the epistemological limits of narrative. The novel’s cryptic allusions to specific writers, musical compositions, venues, and historical events function like hyperlinks inserted into a web-based text, inviting us to investigate their significance to the plot by Googling them online. Cole also uses digital resources as prosthetic devices to augment the sensory experience of silent reading. When the links introduce us to actual figures, like the contemporary French philosophers Badiou and Serres, whose work we can then Google, they encourage us to fantasize that we, too, can aspire to the narrator’s capacious knowledge even though, like the figure of son in the oedipal encounter, we never quite believe that we can best him. However, our sense of inadequacy in the face of the narrator’s seemingly infinite networks of information turns out to be inconsequential. Instead, the tensions between each chapter’s internal coherence and the conceptual links between the chapters open up cracks in the consistency of the presentation itself. Julius’s mastery of the conditions of knowledge associated with art, politics and science come under pressure as his incapacity to articulate the condition of love looms large. Once we supplant Julius as the ethical framer at the novel’s sensate core, however, we become in our turn “the subject presumed to know” and must grapple with the void that inevitably threatens to destabilize that subject.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".