Redesigning Success: How Post-Growth Economics Can Reshape the Games Industry
Bibliographic record
Abstract
tool-it can become an instrument for change.Post-growth economics is a field that seeks to shift the workings of our society away from endless expansion [1].It challenges the assumption that GDP is a worthy indicator of health and happiness, and it stresses the danger of environmental destruction.Ideally, it seeks to create socioeconomic stability and high living standards for all while preserving the planet we call home.It is easy to see why we should care about this goal as people, but why should we care as game developers?Creating games is wonderful.Working in the industry, however, is fraught with precarity.Will your game sell?Will it be successful enough to please your shareholders or to keep your job?We exist in an economy that demands these sales constantly increase.Falter in your compliance, and your work, lifestyle, and home could be in jeopardy.Many of us live with these fears daily, yet we continue to find joy in our work.To paraphrase Romain Rolland and Antonio Gramsci, the pessimism of our intellect is met with the optimism of our will.That optimism doesn't need to be relegated to a survival C Insights → Game developers are driven by remarkable passion, but the industry's growth motives often harm workers and players.→ The games industry can learn from the elements of post-growth economics that prioritize social, economic, and environmental sustainability.→ We can work toward better lives, better games, and a healthier community by shifting our definition of success.
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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".