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Record W792878854

Networks, Innovation and Public Policy: Politicians, Bureaucrats and the Pathways to Change Inside Government

2012· article· en· W792878854 on OpenAlexvenueno aff
Robyn Keast

Bibliographic record

Venue˜The œinnovation journal · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)CLARIONDirectivePublic sectorPublic policyWorkforcePublic relationsPublic administrationPolitical scienceSociologyEconomicsEconomic growthLaw
DOInot available

Abstract

fetched live from OpenAlex

Mark Considine, Jenny Lewis and Damon Alexander Networks, Innovation and Public Policy: Politicians, Bureaucrats and the Pathways to Change inside Government Houndsmills, UK: Palgrave Macmillan, 2009Reviewed by Robyn KeastHaving wrung the most from workforce and workplace productivity initiaitves, innovation has come to the fore as a key goal and directive for public sector organisations to become more efficient. This clarion call for innovation can be heard all around the world, with public services everywhere taking up the message to develop better, smarter, novel, more innovative processes, programs and policies. In the current push for innovation, networks are considered to be a superior vehicle through which collective knowledge can be shared and leveraged; replacing or at least supplementing the role function previously provided by inventive leaders.While a number of studies (for example, Ferlie et al, 1984, Osborne, 1998; Damanpour, Walker and Avellanda, 2009; Walker, Jeans and Rowlands, 2001; Osborne and Brown 2005) have been undertaken to better understand and enhance innovation within the public arena, they have largely overlooked the detailed functioning of networks as innovation drivers or creators. This study by Considine et al. (2009) which examined the norms, practices and structures of innovation networks within the Australian public sector, represents a comparatively rare effort to interrogate this phenomenon and, in so doing, expand understandings of what constitutes and facilitates innovations through government-based networks.The book provides a timely departure from standard, single dimensional approaches through two means. First it synthesises governmental innovation with social network literature and concepts to account for the impact of institutionalised roles and rules on the interpersonal network at play in the innovation process. Second, the authors expertly draw upon and thread social network analysis maps and metrics throughout the text to transform abstract metaphors of innovation networks into more concrete examples, thus highlighting the varying patterns of relationship, exchange and structures in place and functioning within and across the public sectors. Social network analysis (SNA) is an empirical approach that uncovers the hidden topology of exchange patterns that occur between people and entities.Drawing on a substantial data set (qualitative and SNA) collected across eleven diverse municipalities in Victoria, Australia, the authors provide detailed and nuanced insights into the ways in which people and process interact to create innovation and innovation spaces. Therefore, while the introductory chapters, which provide the conceptual, theoretical and methodological foundations to the book, are instructive, the primary intellectual contribution is contained within the latter chapters. A deeper discussion and critique of the underpinning assumptions of how networks create innovation would provide a stronger argumentation in this preliminary section.In Chapter Four the authors set out the background or context within which innovation takes place - the preconditions. It also tracks the flows of information and advice-seeking that occurs between government actors, demonstrating the different network structures that abound as well as the different positions and roles that key innovators/actors occupy. The results distil a more complex picture of innovation creation than the linear models often presented, limiting the capacity for wholesale prescription of network forms and functions. …

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)0.007
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0090.032
Scholarly communication0.0220.025
Open science0.0010.008
Research integrity0.0100.008
Insufficient payload (model declined to judge)0.0100.001

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 head0.054
GPT teacher head0.252
Teacher spread0.198 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations23
Published2012
Admission routes1
Has abstractyes

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