Bibliographic record
Abstract
Video games are often recognized as ephemeral arrangements of signs and symbols, arranged to mimic 'real' relationships of domination and subjection.The fear, then, is that the subjects produced by video games are habituated, in a straightforward way, toward certain dispositions.Yet when we look at a competitive game like League of Legends, we see an active player-subject, engaged in an entrepreneurial project of selfimprovement.This investigation is aimed at power beyond manipulation, asking how an emplaced self is made true in-and-through the pursuit of victory.My autoethnographic account looks at how we become the object of our own conditional existence through interpellation and reflexivity.League of Legends stands as an example of a particular type of reflexive subjectification, one in which we draw on prescriptive texts, guides, and techniques of self-improvement in order to shape ourselves in response to a discursive provocation; in response to the current of opposition.My final acknowledgments go to my wife.At my side, Jana was long-suffering and yet kind, overburdened and yet strong, and inexhaustibly supportive.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".