Bibliographic record
Abstract
This article asks whether we need a posthumanist sociology, arguing that such a perspective can export a good deal of useful methodological and theoretical insight into the sociological toolbox. A posthumanist sociology is not a flattened ontology, in which we find agency in all things living and non-living. A posthumanist sociology asks instead what we do with the fundamental question of becoming both more and less human, following a surge of interest in decentring human exceptionalism. Moreover, a posthumanist sociology returns to the question of what it means to be an intersectional being, to proliferate the involvement of entities at the intersections of histories and social structures. Thus, it is a perspective that emerges from within the conditions of related crises, such as the COVID-19 pandemic. This pandemic has highlighted the need to decentre human exceptionalism, raising a challenge for sociologists to return to the premises of what it means to be a social being. In some sense, management of the pandemic already assumes a decentring. This article builds an argument by first reviewing what broadly constitutes a ‘posthumanist’ sociological perspective, then moves on to a case study of the interrelated human and non-human actors that constituted the early days of the COVID-19 pandemic outbreak. The case study usefully marks the intersection between human and non-human bodies as nodes in the interpretive production chain of this global event – one that acknowledges human extensions and connections to multispecies and ecological systems. Such interlinkages become foundational to interrogating what it means to become human in a posthuman world. The article ends on this posthuman question: under the posthuman condition, if we do not discern a difference between the human and other-than-human entities, how will this homogenization affect the human collective ability to enact and maintain cross-species and cross-entity protections?
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.034 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| 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".