Collaborative Nuance: Citation, Difference, and the Friendship of Roland Barthes and Michel Deguy
Bibliographic record
Abstract
The works of Roland Barthes and Michel Deguy are each marked by the inclusion of friends and lovers, expressing a shared resistance to the norms of impersonal, impartial critique and scholarship. Following Barthes and Deguy’s affectionate position, this article troubles the limits of scholarly citational practices by identifying latent collaboration in the sources and language shared among friends. The incomplete record of Barthes and Deguy’s friendship is complemented by a brief sketch of their pursuits of nuanced, indecisive writing, especially evident in the handling of pre-texts like lecture notes and conference talks. The ongoing exchange between this article’s co-authors—preceding and including this collaboration, and, similar to that between the two French thinkers, written and spoken in various forms and proximities—explains and performs the generative nature of Barthes and Deguy’s joint commitment to difference, as shared expertise and political alignment are bracketed in favor of social postures and the possibility of playful connection. A reading of Barthes’s late attraction to the haiku and Deguy’s commentary on this development puts forth poetic or fictive language that is distinct from the arguments and language systems of philosophy, a significant matter as they each pursue nuance in mourning. Taking these systems to be presently and perhaps necessarily incomplete, the co-authors gesture to a collaborative practice that is drifting and active, privileging the social over ‘loyalty to the idea’ as the basis of creativity and community.
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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.028 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| 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".