The Trouble with Innovation: Why Cleaning Up the Environment Is Going to Be a Lot More Challenging than We Think
Bibliographic record
Abstract
History is replete with evidence that innovation can improve our lives in some circumstances and devastate them in others. The irony of our times is that as our economies become more and more service driven, we are producing, transporting, and consuming more resource- and energy-intensive material goods than ever before in ever more innovative ways. And herein lies the motivation for policy. Innovation can increase welfare, but only within socio-political environments that can respond creatively to its dual nature. Innovating is usually very hard to do, but sustaining human welfare through innovation is always even harder. Thus, for policy makers, the point is not just to increase the incidence of innovation but also to use it to leverage positive social outcomes. I propose that deploying innovation as a strategy for mitigating threats to the environment will demand that we get to grips with it from this perspective. Democracy is a public conversation about innovation, and in the Canadian case, achieving sustainability goals will be impossible unless public institutions are rehabilitated as agents of innovation.
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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.057 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".