Bibliographic record
Abstract
The Science Policy Research Unit (SPRU) at the University of Sussex played a significant role in establishing innovation studies as a field and in establishing innovation policy as a framework for governmental thinking. But the SPRU’s first book was not directly about innovation; it was “Thinking about the future: a critique of the Limits to Growth” (1973). The “Limits to Growth” (1972) had been an attempt by researchers at MIT, funded by the Club of Rome and the Volkswagen Foundation, to quantitatively falsify the idea of endless growth on a finite planet. The SPRU’s response—later reprinted with the title “Models of Doom” outside the UK—was one of many that framed “Limits” as overly pessimistic. This paper considers the impact of this work on the developing fields of innovation research and policy. It takes a critical ethnostatistics approach to the modelling practices deployed by these two very different groups of professional social scientists. It focuses on two methodological moves made by the SPRU researchers. First, this paper shows how the SPRU arguments established a fetish for data precision—a standard that the MIT team rejected, but one that carried on through the SPRU’s further work into innovation research and policy. Next, it discusses how the SPRU researchers (role)modelled mathematical faith in socio-technical change. This was more than a techno-optimist (or Promethean) stance. It established a norm of toxic positivity around questions of technology, innovation, and the environment. These two methodological moves—fetishizing data precision while asserting toxically positive Prometheanism—became cultural memes that carried forward from this debate into innovation policy, modelling, and statistical practices. In short, the pro-innovation econometrics developed for this debate by the SPRU researchers had a lasting impact on the epistemic culture of innovation studies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".