Bibliographic record
Abstract
Minecraft play practices reveal a type of analytic play in which significant work is invested in discovering esoteric details about the game, without necessarily providing practical prescriptions for optimizing play. This paper proposes the term “procedural elaboration” to describe such activities and the knowledge thereby produced. In contrast to the existing concept of theorycrafting, the products of procedural elaboration are primarily descriptive rather than prescriptive. However, this knowledge is far from trivial or banal. I argue that these knowledge-making activities can be explained through two functions of procedural elaboration. First, it provides players with a tool for dealing with the threatening inscrutability of some procedural game systems. Second, it acts as a ritual form of communication that helps to solidify a coherent Minecraft player community, while also establishing a social order within that community. Subsequently, I consider why players persist in using specifically experimental methods in procedural elaboration, even though the online availability of decompiled Minecraft source code means that the rules are not fully hidden as they are in most other games. I argue that the experimental method persists for these reasons: because it does not require specialized programming skills; because the gameplay already casts scientific experimentation as play; and because the iterative nature of Minecraft’s development has produced source code that is structured in a way that resists direct deciphering.
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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".