Bibliographic record
Abstract
The concept of the techno-pharmakon, introduced by Plato in Phaedrus and later adopted by Jacques Derrida, reached the apex of its philosophical utility in the work of Bernard Stiegler. The simple idea that technology can either be a remedy or a poison, an idea central to Stiegler’s work, is an irresistible binary for media theorists. This essay begins with a critical reflection on that useful binary and on the white male legacy of the pharmakon itself, a legacy that I confess to have perpetuated. If technology can either cure or kill, it does not do so equally; rather, the pharmakon achieves radical variability depending on factors of race, gender, and ability. Put simply, this essay argues that the very same systemic power asymmetries that are responsible for our most pressing social problems are embedded in our technological systems. Moreover, these same power asymmetries haunt media theory itself, leading me to argue that the pharmakon needs to be troubled. I draw carefully on queer theory to recommend a path toward this disturbance of the power systems embedded in both technoculture and in media theory.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.053 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".