From <i>The Limits to Growth</i> to Greenhouse Gas Emissions Pathways: Technological Change in Global Computer Models (1972–2007)
Bibliographic record
Abstract
From the World2 and World3 models to contemporary Integrated Assessment Models (IAMs) that model carbon neutral emission pathways, global computer models have served as virtual laboratories for addressing economic, environmental and technological concerns together. Representing technological change has been a controversial element of global modelling efforts because, to a great extent, it sets the parameters on conceivable futures. To retrace this history, this article analyses four moments when modellers debated technological change: the controversy spurred by The Limits to Growth in the 1970s; subsequent global future studies during that decade; the IIASA Energy in a Finite World study in the 1980s; and the shift to endogenous technological change in IAMs in the 2000s. It shows that the notion of technological change as a predictable parameter affecting the future of society was not a given. Technological change progressively became a parameter in models as more elaborate methodologies were developed to simulate it. When modellers began to focus on climate action in the 1990s and 2000s, their interest in the relationship between technological change and social change dwindled. The increasing skill with which modellers formally represented technological dynamics was commensurate to the decline of heated discussions over how conflicting worldviews shaped simulations.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".