Betting on DOTA 2’s Battle Pass: Gamblification and productivity in play
Bibliographic record
Abstract
The transformation of games with the advent of platformized distribution systems continues to produce new and agile forms of consumption and exploitation. Valve Corporation’s DOTA 2 is a key example of a gaming space that is constantly atomized and rebuilt with the aim of optimizing player participation. This participatory form is ever-more gamblified and framed by systems designed to habituate players to a new form of consumption. This article explores how DOTA 2 transforms every year with the advent of a yearly Battle Pass, brimming with gambling systems aimed at eliciting specific forms of user participation. We catalog and schematize these systems with the aim of shedding light on the inner workings of DOTA 2 during this season. The purpose of our work is to move the discussion beyond a regulatory focus on symptomatic loot boxes and toward a deeper understanding of the rhetorical systems hiding beneath game systems.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".