Theorising a spectrum of reasons for failure in knowledge brokering: a developmental evaluation
Bibliographic record
Abstract
Background: Despite the growing interest in knowledge brokering as an approach to mobilising knowledge for societal benefit, research has only recently begun investigating the consequences of knowledge brokers' actions. In particular, while it is known that failure can provide a valuable learning experience for individuals and organisations alike, very few studies have explored failure in knowledge brokering. Aims and objectives: With the aim of informing how knowledge brokers can navigate common professional challenges, this article discusses a spectrum of reasons for failure in the professional practice of knowledge brokering. Methods: Data for this article comes from a developmental evaluation of a network of knowledge brokers, focusing specifically on a set of semi-structured interviews (N = 20). Findings: The overarching themes identified in this study are organised according to the three constructs that knowledge brokers act upon in the i-PARIHS framework: innovation, recipients, and context. Knowledge brokers experienced different types of failure across these constructs, ranging from violations of prescribed practices and processes to exploratory testing. Discussion and conclusions: Failure is not a monolithic outcome, and future research could seek to identify levers for change regarding the failure experiences available to knowledge brokers. Additionally, it will not be enough to learn to fail; research and practice in knowledge brokering must learn to fail intelligently. Finally, failure cannot remain a private experience. Advancing the field of knowledge brokering will require researchers and practitioners to share when and how their efforts have failed.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".