Bibliographic record
Abstract
Abstract The notion of the cyborg is a useful tool for examining the relations between gamers and gaming environments, and the construction of narrative in gaming contexts. I will discuss the idea of the gamer’s sense of self, projected into the game-world exploring the extended mind theory and proprioception. Following that I will address the construction of narrative with algorithmic (computational) interaction with the database and procedural narrative in the sense of how the gamer relates to both the real and virtual world. According to Donna Haraway in her book, Simians, Cyborgs, and Women, ‘a cyborg is a hybrid creature, composed of organism and machine’ (1991: 1). Gabriella Giannachi embellishes Haraway’s definition in that the cyborg is ‘able to bridge the gap between the real and representation, between social reality and fiction’ (2004: 47). I intend to demonstrate that the material gamer interfaces with the database/game/internet and the projected identity/presence/proprioception of the gamer becomes part of the virtual world. The narrative is virtual and interaction with the algorithms (rules) provides a sense of agency within the game world. Procedural narrative is created through an algorithmic interface with a database. The human is subsumed into the technological. Human interaction becomes algorithmic. The pleasure of augmentation is seductive.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".