Bibliographic record
Abstract
Prominent software executives have vocally proclaimed that tech such as AI and space exploration will resolve earthlings’ sustainability crisis. Common to these proposals is a paradigm of ever-increasing human demands met by ever-increasing technological scope, capacity and surveillant efficiency. Advocates (Diamantis 2016, Andreessen 2023) label this paradigm abundance. Software practitioners, who collaborate to create the functionality on which these advocates’ platforms are built, also employ a notion of abundance — often in their public commentary, and especially consistently in their conventional practices — but of a very different sort. This paper uses content analysis of more than two hundred public statements (microblogs, blog posts and publications) by software practitioners and executives, plus 17 published assessments of AI assistant software's impact on development work, alongside praxiographic analysis (Mol 2002) of conventional cross-disciplinary software team practices (such as debugging, user-centered design and sprint retrospectives), to discern an ecosystemic understanding of abundance enacted in software team practice. It then contrasts the two versions of abundance and suggests the potential benefit of the one hidden at the heart of collaborative software practice to the project of civil and environmental sustainability on earth.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 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".