Biosemiotics for postdigital living: the implications of the implications
Bibliographic record
Abstract
Abstract The postdigital condition is discussed from the perspective of Paul Cobley’s biosemiotic approach to culture. While semiotics is often concerned with cultural criticism, there has been no explicit biosemiotic approach to culture, until only recently with Cobley unfurling such a research program. The key to this is the biosemiotic notion of modeling , which accounts for co-evolutionary processes encompassing biology and culture. This approach responds to recent calls in the humanities and social sciences to understand culture as constituted through technology, but also as something not strictly human (more-than-human). By undermining both vitalism and reductionism, biosemiotics avoids biologism and culturalism, which is of much importance for theorizing culture and learning in light of evolution. This has consequences for construing cultural pluralism. Mainstream notions of multiculturalism rely on cultural holism and, hence, advocate the separation of communities and languages for the pretense of maintaining diversity. Cobley’s theory avoids this pitfall, offering a view of cultures as intrinsically heterogeneous and open systems. This suggests further implications for how we understand the aims of literacy and state-run education. We present an account of biocultural learning that accommodates contemporary posthumanist and postdigital orientations. Construing learning as ecologically contextual is necessary for addressing ongoing technological transformations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.047 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".