Embedding Innovation: How Large Organizations Can Succeed at Innovation in the Long Term
Bibliographic record
Abstract
Making innovation stick has proven difficult to many large organizations. The challenge these organizations face is to turn innovation from a buzzword into a systemic and widely distributed competency. This study explored how to do this by asking the question “How might large organizations enable and nurture innovation over the long term?” \n \nThe study used a combination of research methods and adopted a design approach to answering the question. Research methods included literature review, case studies, surveys, semi-structured interviews, innovation canvassing and foresighting. The research identified that in order to embed innovation into an organization’s DNA, that organization must have a strong innovation orientation, and must demonstrate aptitude in five critical areas: strategy, culture, process, portfolio and scalability. These findings were used to propose a roadmap to innovation for the City of Toronto’s Chief Corporate Officer; one that embodies the characteristics of successful, long-term approaches to innovation that would allow the organization to transform itself into a more innovative organization.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".