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Record W3188307615 · doi:10.22215/etd/2019-13664

Providing Science Advice: An Ethnography of the Council of Canadian Academies’ Boundary Work of Recontextualizing Expert-Produced Scientific Knowledge for Canadian Government Policy-Makers

2019· dissertation· en· W3188307615 on OpenAlexaffabout
Matthew Falconer

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsCarleton University
FundersRoyal Society
KeywordsBoundary objectRhetorical questionBoundary-workEthnographyGovernment (linguistics)DisciplinePublic relationsWork (physics)Political scienceSociologyKnowledge managementEngineeringSocial scienceComputer scienceNegotiation

Abstract

fetched live from OpenAlex

My study is an ethnographic account of the collaborative discursive activity of the Council of Canadian Academies (CCA), a non-profit advisory organization that contracts with government clients to perform "boundary work" which includes taking expert-produced scientific knowledge and transforming and re-purposing this knowledge into a science-based discourse that is accessible and useful for government policy-makers.The CCA works at armslength from its government clients in producing science-related information for policy-makers, an activity that draws on the expertise of a multi-disciplinary panel of invited outside experts and the CCA staff.Using data collected at the CCA between 2013 and 2018, I explore the organization's culture, with a focus on its cultural constructs and tools.More specifically, its representations of key entities such as "science", "evidence", and "expertise".Concentrating my analysis on a 2017 CCA report produced for Transport Canada titled Older Canadians on the Move, I unpack the discursive "black box" of what is referred to in the CCA as the "Council Assessment Lifecycle Methodology" (CALM), a cultural tool used by a CCA "staff assessment team" in "recontextualizing" expert-produced science for use by policy-makers.In describing this collaborative activity, I show how aspects of the culture shape the boundary work performed by the panel and the staff team.Additionally, I use the method of "textography" to identify the types and rhetorical purposes of a series of "intermediary texts" used in producing the report, Older Canadians on the Move.I found that the staff team employed multiple iterations of intermediary texts to move the McKelvey, 2018; Hutchings & Stenseth, 2016; OECD, 2015).One approach used in providing science-informed advice to governments is through an advisory body closely connected with a national academy of science.Such an advisory body functions as an intermediary, nongovernment organization (NGO) mandated to interpret expert-produced scientific knowledge and communicate the relevant meaning of this expert-produced knowledge for use by government policy-makers in a form that is accessible and useful to them (Gluckman, 2014).Two well-known examples of such organizations are the National Academies of Science in the United States' National Research Council and the Royal Society in England's Science Policy Expert Advisory Committee.In Canada, the Council of Canadian Academies (CCA)performs this role by providing science-based advice to Canadian government policy-makers in the form of advisory reports, a kind of "boundary object" (Star & Gruesemer, 1989) -that is, an artefact, such as a report, that crosses the boundary of science or policy and is useable in either domain without becoming something entirely new.Government policy-making is a multi-faceted, solutions-oriented practice in which policy-makers attempt to resolve societal issues (Cairney, 2016;Tehara, 2010;Theodoulou, 2013).In Canada, as elsewhere, policy-making is a political process involving elected and unelected officials in national, provincial, and municipal institutions.At the federal level, Canadian policy is enacted through an Act of Parliament, which involves a process of deliberation among elected Members of Parliament sitting in the House of Commons and unelected Senators in the House of the Senate (Bejermi, 2010).Policy-making is an activity in which policy-makers use this knowledge to identify and choose between alternative ends and means while weighing competing factors such as public values, cost, efficiency, security, and liberty (Douglas, 2009;Dunn, 2013).For example, articles cited were from the following types of academic disciplines:  The health-sciences and medicine (Maturitas,

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaScience and technology studies
Domain: not available · Genre: Other
About the Canadian research system: yes · About a Canadian topic: yes
Qualitativehigh
gptScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: yes · About a Canadian topic: yes
Qualitativehigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.024
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.850

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.061
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.010
Science and technology studies0.0800.044
Scholarly communication0.0140.008
Open science0.0060.015
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0060.001

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 head0.053
GPT teacher head0.319
Teacher spread0.266 · 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

Labeled directly by 2 models reading the full record.

Study designQualitative
Domainnot available
GenreOther · Empirical

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
Published2019
Admission routes2
Has abstractyes

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