Examining Open Innovation in Science (OIS): what Open Innovation can and cannot offer the science of science
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Scholars across disciplines increasingly hear calls for more open and collaborative approaches to scientific research. The concept of Open Innovation in Science (OIS) provides a framework that integrates dispersed research efforts aiming to understand the antecedents, contingencies, and consequences of applying open and collaborative research practices. While the OIS framework has already been taken up by science of science scholars, its conceptual underpinnings require further specification. In this essay, we critically examine the OIS concept and bring to light two key aspects: 1) how OIS builds upon Open Innovation (OI) research by adopting its attention to boundary-crossing knowledge flows and by adapting other concepts developed and researched in OI to the science context, as exemplified by two OIS cases in the area of research funding; 2) how OIS conceptualises knowledge flows across boundaries. While OI typically focuses on well-defined organisational boundaries, we argue that blurry and even invisible boundaries between communities of practice may more strongly constrain flows of knowledge related to openness and collaboration in science. Given the uptake of this concept, this essay brings needed clarity to the meaning of OIS, which has no particular normative orientation towards a close coupling between science and industry. We end by outlining the essay's contributions to OI and the science of science, as well as to science practitioners.
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.
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 | 0.030 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.116 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.023 | 0.138 |
| Open science | 0.010 | 0.013 |
| 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 it