Bibliographic record
Abstract
Since the 1930s, physicists have known that a great deal of matter is missing from their observations. But even the best scientific instruments currently available cannot directly observe dark matter. There is a similar problem in research on innovation. But unlike physics, where an average of three new papers per day are focused on the elusiveness of dark matter, hardly anyone is systematically working to reveal dark innovation. This book argues that the problem rests in disciplinary conventions. The common tools and techniques of innovation research were built with only certain forms of innovation in mind. They conceal as much as they reveal. This is demonstrated through an exploration of the neoliberal market biases inscribed within innovation models, contextual histories, metanarratives, classification systems, regional topologies and statistical methods. These instrumentalities are reworked to reveal how public organizations on Canada's Atlantic coast have developed novel technological goods. This is despite definitive claims in the literature that innovation in goods is the exclusive domain of the private sector. Here, innovation in ocean science instruments serves as an empirical motif for exploring the instrumental biases in innovation research. And this empirical work serves a broader purpose: reframing the notion of 'dark innovation' as a call for critical scholars to deconstruct the central assumptions of innovation studies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.868 | 0.821 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".