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Enregistrement 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 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity of Alberta Library · 2022
Typedissertation
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueMycorrhizal Fungi and Plant Interactions
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésTerminologyGovernment (linguistics)Field (mathematics)Mental healthAphasiaPublic policyPoint (geometry)AddictionTranslation (biology)Translational research

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,181
score de la tête « metaresearch » (Gemma)0,367
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesCommunication savante
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,974
Score d'incertitude au seuil0,956

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,1810,367
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0070,008
Études des sciences et des technologies0,0170,048
Communication savante0,0260,028
Science ouverte0,0030,016
Intégrité de la recherche0,0060,009
Charge utile insuffisante (le modèle a refusé de juger)0,0030,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,026
Tête enseignante GPT0,268
Écart entre enseignants0,242 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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