Toward More Inclusive Networks and Initiatives in Innovation Ecosystems: Protocol for a Systematic Review
Bibliographic record
Abstract
BACKGROUND: Expanding the cooperation and enlarging the participation of more diverse stakeholders within innovation ecosystems will increase their efficiency and capacity to contribute at local, regional, and national levels. OBJECTIVE: This paper presents the protocol for a systematic review that will identify "opening-up" strategies of innovation ecosystems for increasing the participation of more diverse innovation stakeholders, particularly from low-innovation countries, during the ecosystem formation period. METHODS: An algorithmic search in 4 databases (Web of Science, Cochrane Library, Scopus, and Social Science Research Network) will be applied based on the PerSPecTIF (perspective, setting, phenomenon of interest/problem, environment, optional comparison, time/timing, and findings) methodology, the Cochrane guidelines for qualitative evidence synthesis, and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Selection criteria for eligibility include peer-reviewed articles published after December 31, 1999, and containing original data. No restrictions will be placed on the article language and study region, design, or methodology. Methodological strengths and limitations will be assessed using the Critical Appraisal Skills Programme tool. The thematic synthesis method will be adopted, and the GRADE-CERQual tool will be used to assess confidence. RESULTS: A preliminary search in Web of Science revealed 2758 records. This work is part of the ANGIE project, which was funded by the European Union's Horizon 2020 research and innovation program (grant 952152) in January 2021. We anticipate that the results of this systematic review will be published in spring 2022. CONCLUSIONS: We anticipate that the outcomes of this systematic review will outline the best practices used by initiatives and networks, as well as their impacts on creating larger and more inclusive ecosystems. TRIAL REGISTRATION: OSF Registries osf.io/hc62k 10.17605/OSF.IO/HC62K. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/34071.
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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 | 0.164 | 0.181 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.016 | 0.014 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.064 | 0.013 |
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".