Reassembling Knowledge Translation Through a Case of Autism Genomics: Multiplicity and Coordination Amidst Practiced Actor-Networks
Bibliographic record
Abstract
Knowledge translation (KT) has become a ubiquitous and important component within the Canadian health research funding environment. Despite a large and burgeoning literature on the topic of KT, research on the science of KT spans a very narrow philosophical spectrum, with published studies almost exclusively positioned within positivism. Grounded in a constructionist philosophical position and influenced by actor-network theory, this dissertation aims to contribute to the Canadian KT discussion by imagining new possibilities for conceptualizing KT.\nThis is an empirical-theoretical study which is based on eight months of data collection, including interviews, participant observation, and document analysis. This data collection took place in a basic science laboratory, a clinic, and amongst families involved in genomic research pertaining to Autism Spectrum Disorder in a Canadian city. Interviews were transcribed verbatim and organization of the data was aided by QSR Nvivo software. Theoretical insights put forward in this dissertation are based on a detailed description of the everyday, local, micro-dynamics of knowledge translation within a particular case study of an autism genomics project. Through data collection I have followed the practices of a laboratory, clinic, and family homes through which genomic knowledge was assembled and re-assembled.\nThrough the exploration of the practices of scientists, clinicians, and families involved in an autism genetics study, I examine the concepts of multiplicity, difference, and coordination. I argue that autism is practiced differently, through different technologies and assessments, in the laboratory, clinic, and home. This dissertation closes with a new framework for and model of the knowledge translation process called the Local Translations of Knowledge in Practice model. I argue that expanding the range of theoretical and philosophical positions attended to in KT research will contribute to a richer understanding of the KT process and move forward the Canadian KT agenda. Ethics approval for this research was obtained from The University of Western Ontario and from the hospital in which the data was gathered.
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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 | 0.028 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.043 | 0.076 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".