Bibliographic record
Abstract
Abstract Since the 2010s, auto/biography studies have engaged in productive explorations of its intersections with theories of posthumanism. In unsettling concepts of the human, the agential speaking subject seen as central to autobiographical acts, posthumanism challenges core concerns of auto/biography (and humanism), including identity, agency, ethics, and relationality, and traditional expectations of auto/biographical narrative as focused on a (human) life, often singular and exceptional, chronicling a narrative of progress over time—the figure and product of the liberal humanist subject that posthumanism and autobiography studies have both critiqued. In its place, the posthuman autobiographical subject holds distributed, relativized agency as a member of a network through which it is co-constituted, a network that includes humans and non-humans in unhierarchized relations. Posthuman theories of autobiography examine how such webs of relation might shift understanding of the production and reception of an autobiographer and text. In digital posthuman autobiography, the auto/biographer is working in multimodal ways, across platforms, shaping and shaped by the affordances of these sites, continually in the process of becoming through dynamic engagement and interaction with the rest of the network. The human-machinic interface of such digital texts and spaces illustrates the rethinking required to account for the relational, networked subjectivity and texts that are evolving within digital platforms and practices. The role of algorithms and datafication—the process through which experiences, knowledge, and lives are turned into data—as corporate, non-consensual co-authors of online auto/biographical texts particularly raises questions about the limits and agency of the human and the auto/biographical, with software not only coaxing, coercing, and coaching certain kinds of self-representation, but also, through the aggregating process of big data, creating its own versions of subjects for its own purposes. Data portraits, data mining, and data doubles are representations based on auto/biographical source texts, but not ones the original subject or their communities have imagined for themselves. However, the affordances and collaborations created by participation in the digital web also foster a networked agency through which individuals-in-relation can testify to and document experience in collective ways, working within and beyond the norms imagined by the corporate and machinic. The potential for posthuman testimony and the proliferation of autobiographical moments or “small data” suggest the potential of digital autobiographical practices to articulate what it means to be a human-in-relation, to be alive in a network.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.005 |
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".