Bibliographic record
Abstract
On ref lecting upon the hundred-plus hours that the average gamer spends in playing games like Fallout 3 (Bethesda Game Studios 2008), it seems strange that one would like to spend so much time roaming a virtual post-apocalyptic wasteland.Given the recent popularity of the wasteland setting in videogames, such as Fallout 3, S.T.A.L.K.E.R.: Shadow of Chernobyl (GSC Game World 2007) and Borderlands (Gearbox Software 2009), it might be worth asking what makes wastelands so interesting to the gaming community.Post-apocalyptic wastelands are a popular trope in Science Fiction on which all of the above games as well as others such as Half-Life (Valve Corporation 1998) and BioShock (2K Boston 2007), with their dystopian environments, heavily draw on.However, that is not the only reason: even a game like Far Cry 2 (Ubisoft Montreal 2008), where the player drives through seventy miles of African bush, offers an experience similar to the wanderings of Fallout 3's protagonist.This is the experience of travelling in a world fraught with danger and uncertainty through wide expanses of game space interspersed with nodes of activity.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".