Dead Memories: Heidegger, Stiegler, and the Technics of Books and Libraries
Bibliographic record
Abstract
In this paper, I attempt to understand Heidegger’s conception of technology in light of Stiegler’s critique of that conception, anchored in a discussion of books and libraries as technological artefacts. I argue, following Stiegler, that Heidegger did not adequately take into account the inherent technological character of the means by which Dasein’s heritage is transmitted to subsequent generations. Stiegler’s concept of epiphylogenesis—dead matter externally organized to support living, internal memory and instantiated in books and libraries—is therefore a useful supplement to the Heideggerian account of the transmission of heritage. I examine the points where Heidegger mentions books and/or libraries in three exemplary texts from the beginning, middle, and end of his career, indicating at each point how Stiegler’s thought can supplement that of Heidegger. I conclude with a brief discussion of David Mitchell’s novel Cloud Atlas, considered as a paradigmatic example of Stiegler’s conceptual framework.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".