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Record W7161981612 · doi:10.82308/48021

Exploring the public value of networked science in the Canadian Arctic

2021· dissertation· en· W7161981612 on OpenAlexaboutno aff
Ashlee-Ann Pigford

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate governanceArcticValue (mathematics)Public valueIndigenousNetwork governancePublic policySustainable developmentAdaptation (eye)Salient

Abstract

fetched live from OpenAlex

The Arctic is one of the world’s most rapidly changing regions and is facing a series of unprecedented and complex challenges. It has been argued that science-informed innovation will be key in supporting sustainable regional development and improved policy outcomes. Despite significant and increasing public investment in Arctic research, Northern communities continue to assert that existing research governance structures have been unable to create public value, failing to deliver research that reflects public expectations, interests, and innovation needs. Given that little is known about how Arctic scientific research is embedded in broader innovation and value creation processes, this dissertation takes a systems approach to examine the complex and dynamic governance contexts that shape how networked scientific research creates public value in the Canadian Arctic. It begins with a literature review that connects the concepts of innovation ecosystems and public value with Canada’s efforts to guide Northern and Arctic research to identify salient challenges and opportunities relevant to research and innovation policy. Then, the remainder of the dissertation examines public value creation processes by focusing on the instrumental case of ArcticNet, a large Canadian research network responsible for connecting public, private, government, not-for-profit and Indigenous stakeholders to study the impacts of climate change in the Arctic with the goal of informing adaptation strategies and national policies. This empirical research focused on three levels of organization: 1) networked scientific research actors; 2) a network administrative organization; and 3) institutional mechanisms for delegating authority. A Social Network Analysis was conducted to map the configuration of science-based innovation actors in ArcticNet and its evolution over a 13-year period. Results suggest that the network was centralized around non-local public-sector actors who played central boundary spanning roles that facilitated collaboration, while local Arctic actors had an increasing propensity for carrying out boundary spanning roles and closing structural holes in the network. Next, the Network Administrative Organization (NAO) was used as the unit of analysis to explore the network-level public values associated with ArcticNet to inform network-level evaluation strategies. Public Value Mapping revealed that the NAO targeted diverse publics, seeking to create a range of public values that were identified both at the outset of the network and emerging later. Results point to the need for research networks to improve clarity in value articulation across public facing documents and different scales (e.g., research versus network impacts). Turning to the larger contract between science and society, principal-agent theory and the public value Strategic Triangle were used to identify the overlapping, multi-level principal-agent contracts for delegating public value creation in Arctic science. Findings illustrate that the adoption of networked models for science governance corresponded with a trend towards contracting roles for public value management to Arctic scientific research actors; however, it remains unclear how core elements of public value management (i.e., identifying public value, political legitimacy and operational capacity) have been realized. This dissertation presents new insights into the complex, networked and multi-dimensional nature of Arctic scientific research governance in Canada, raising important questions about how publicly-funded research efforts can be designed to enhance public value, with potential implications for the strategic design and operation of Arctic research efforts, as well as for regional research and innovation policy

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)0.006
metaresearch head score (Gemma)0.013
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 score0.984
Threshold uncertainty score0.966

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.007
Science and technology studies0.0160.011
Scholarly communication0.0140.004
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.113
GPT teacher head0.343
Teacher spread0.230 · 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
Published2021
Admission routes1
Has abstractyes

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