Bibliographic record
Abstract
There is something happening here. What it is ain't exactly clear. It might feel good, it might sound a little sumpun', but damn the game if it don't mean nothin'. What is game? Who got game? Where's the game in life behind the game behind the game? I got game. She's got game. We got game. They got game. He got game. It might feel good, Or sound a little sumpun', But f – the game if it ain't saying nothin'. Public Enemy and Stephen Stills, He Got Game ‘WHAT IS GAME? WHO GOT GAME?’ In the 1950s Hannah Arendt began to focus on a specific aspect of politics. Instead of looking at the institutions, routines and policies of governance on the one hand, or on the great political theories on the other, she aimed to concentrate on or ‘confront’ the activity of politics itself. In doing this, she drew attention to a specific kind of game-like activity that occasionally emerges in the broader field of politics and government. She associated it with the Greeks and certain moments in the history of Western politics, especially but not exclusively revolutionary times, and claimed that it is the very ‘ raison d’être of politics'. For Arendt, four characteristics of this unique political game are of paramount importance. Firstly, the activity consists in interaction among equal citizens with different viewpoints on their common world and who engage in agonistic activities for recognition and rule in public space.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".