Bibliographic record
Abstract
This is a short introduction to a series of blog posts, below, written by participants in the second edition of the GameBling Game Jam. GameBling 2.0On the 11th and 12th of February 2023, Concordia University hosted the second edition of the GameBling Game Jam, one year after the successful first edition (Hoebanx et al., 2023).A game jam is an event during which individuals or teams attempt to create a game from scratch in a limited amount of time.A detailed explanation of game jams and a summary of the first edition can be found in Hoebanx et al. (2023).In Hoebanx et al. (2023), we argued that game jams can be used as an innovative research method "that can help uncover new ways to think about and question social science concepts."We put that idea to the test again in the second edition, with an added twist: we held a writing workshop after the event to which all jam participants were invited.Of the 16 original participants, 9 participated in the writing workshop.The primary goal was to encourage jam participants to reflect on and write about their experiences as game designers, aiming to gain insights into their thinking and design processes-something that last year's blog post was not able to achieve (Hoebanx et al., 2023).The blog posts in this series are the result of this writing workshop.While the theme of the first edition was slot machines, the second edition's theme was more abstract: (Un)Lucky.
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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".