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Record W6940459497 · doi:10.7939/r3-4caf-xd43

Speaking Scientifically: The Role of Communication in the Translation of Novel Brain Science Research into Policy

2022· dissertation· en· W6940459497 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2022
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsTerminologyGovernment (linguistics)Field (mathematics)Mental healthAphasiaPublic policyPoint (geometry)AddictionTranslation (biology)Translational research

Abstract

fetched live from OpenAlex

This project examines how addiction and mental health policy comes to incorporate novel research findings from brain and neurosciences by asking: “What role does communication play in the translation of research into policy?” I use a multiple case design to compare and contrast varying communication patterns involved in the translation of three distinct sets of research findings. Each of the three cases in the dissertation stem from similar, but distinct sets of research findings from brain science, and have been translated into policy to varying degrees, with noticeably different translation paths. These varying patterns of translation are demonstrated in the ways research concepts and terminology are included and communicated within policy documents. My research reveals that communication tools are employed in different ways across the three cases. Analysis was guided by the SPEAKING model from speech code theory in order to systematically analyze and examine policy documents published by Alberta government ministries from 1990 to the present. 1990 marked the beginning of the ‘decade of the brain’, when technological ideas allowed for greatly enhanced brain imaging techniques. From that point forward, researchers became increasingly able to map out and pinpoint various neural pathways and brain regions associated with conditions like addiction and mental illness. Since government policy documents are carefully constructed because they are intended to guide and govern practices on a field level, they serve as an important record over time. In Alberta, these documents are produced by government ministries that are responsible for planning, developing and managing government-operated affairs, including health care and education. Through systematic analysis of policy documents related to each of the cases, I found that the three sets of brain research findings varied in how and to what degree they were translated into addiction and mental health policy. Further, the communication patterns exhibited by each of the cases differed in how these research findings were conveyed. In comparing the communication patterns constituting different dynamic translation processes, I firstly contribute to the literature on translation by developing a process model to show how the deliberate and careful construction of metaphors can act as a robust mechanism for facilitating the travel of ideas between contexts. Secondly, I contribute to this body of scholarship by explicating the nature of editing rules and how they operate in relation to one another during the translation process. Thirdly, I provide a more nuanced explication of the general patterns of communication underlying the translation of knowledge from one context into another. By examining the usage and explanations of research findings across cases, my analyses reveal how communication patterns can constitute different dynamic translation processes. Overall, my research shows that processes of translation can be deployed through specific ways of communicating, and that the ways in which research concepts are explained and edited over time can be accomplished through construction of language that resonates with local audiences to realign perceptions and establish common understandings.

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 imitation

Not 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.

metaresearch head score (Codex)0.181
metaresearch head score (Gemma)0.367
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.974
Threshold uncertainty score0.956

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1810.367
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.008
Science and technology studies0.0170.048
Scholarly communication0.0260.028
Open science0.0030.016
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.026
GPT teacher head0.268
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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