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
Deploying knowledge brokers to bridge the ‘gap’ between researchers and practitioners continues to be seen as an unquestionable enabler of evidence-based practice and is often endorsed uncritically. We explore the ‘dark side’ of knowledge brokering, reflecting on its inherent challenges which we categorise as: (1) tensions between different aspects of brokering; (2) tensions between different types and sources of knowledge; and (3) tensions resulting from the ‘in-between’ position of brokers. As a result of these tensions, individual brokers may struggle to maintain their fragile and ambiguous intermediary position, and some of the knowledge may be lost in the ‘in-between world’, whereby research evidence is transferred to research users without being mobilised in their day-to-day practice. To be effective, brokering requires an amalgamation of several types of knowledge and a multidimensional skill set that needs to be sustained over time. If we want to maximise the impact of research on policy and practice, we should move from deploying individual ‘brokers’ to embracing the collective process of ‘brokering’ supported at the organisational and policy levels.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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