Enhancing Policy Impact Through Knowledge Translation: The Role of Innovative Evidence Products
Bibliographic record
Abstract
The objective of applied social science research is to inform policy and practice to improve societal outcomes (White and Welch 2022).However, in the words of the head of the World Bank's Development Impact Group, DIME, 'Dissemination is dead'. 1 That is, traditional dissemination pathways-academic publications, conference presentations, and even policy briefs-are insufficient for achieving meaningful policy uptake.No matter how rigorous or relevant, research alone does not usually translate into policy impact without deliberate, structured mechanisms to bridge the gap between evidence generation and decision-making.The recognition of this fact has resulted in the growth of knowledge translation as what has been called the fourth wave of the evidence revolution (White 2019). Knowledge translation is 'the exchange, synthesis, and effective communication of reliable and relevant research results.The focus is on promoting interaction among the producers and users of research, removing the barriers to research use, and tailoring information to different target audiences so that effective interventions are used more widely' (World Health Organization 2004).There are various approaches to knowledge brokering.These include direct interaction between researchers and decision-makers in interpreting and using the findings, in-house knowledge brokers, creating a 'helpdesk' function, or using an independent rapid review service. | Evidence Portals: An Emerging ModelEvidence portals provide interactive, web-based platforms that curate and categorize evidence in user-friendly formats.The first examples come from the education sector, starting with the US Institute of Education Science's What Works Clearinghouse, and the Education Endowment Foundation's (EEF) Teaching and Learning Toolkit. 4 The EEF toolkit lists over 30 approaches
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 | 0.346 | 0.499 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.053 | 0.058 |
| Open science | 0.008 | 0.040 |
| Research integrity | 0.019 | 0.019 |
| Insufficient payload (model declined to judge) | 0.023 | 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; 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".