Building entrepreneurial researcher capacity to increase positive changes in practice
Bibliographic record
Abstract
Background: Over the past decade, research-based and evidence-informed decision-making have played increasingly important roles in influencing educational policy and impacting practices in education. The dissemination, implementation and sustainability of research-to-practice are thus fruitful topics for discussion. Even though, as Oliver and Cairney (2019) report, there is no shortage of literature on the topic, many academics struggle with where to start. This entrepreneurial concept is based on the current literature and the author’s experiences working with an Ontario Ministry of Education in Canada initiative designed to promote a systems approach to building research-practitioner partnerships so as to mobilise findings into practice. Aims and objectives: This practice paper is meant to offer such a starting place. It introduces the concept of the `entrepreneurial researcher’ and provides concrete strategies by which contemporary researchers can develop entrepreneurial skills to plan, promote and mobilise their research and findings. In doing so, the researchers may arrive at a better understanding of self-actualization opportunities and move beyond the institutional barriers (i.e. academic institutions), that underlie and incentivise much of scholarly publication, to broaden their research focus and dissemination (Best and Holmes 2010). Key conclusions: We suggest a revised research process that includes the importance and application of collaborative planning, networking, partnerships and knowledge mobilisation processes. Also, recognizing the goals of many research agendas to improve and impact practice, we provide a list of recommendations for researchers to support greater transfer of research into practice.
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 | 0.135 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".