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Record W4255866965 · doi:10.4324/9781315750996

Innovation, Regional Development and the Life Sciences

2016· book· en· W4255866965 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typebook
Languageen
FieldBusiness, Management and Accounting
TopicUniversity-Industry-Government Innovation Models
Canadian institutionsYork University
Fundersnot available
KeywordsRegional scienceEngineering ethicsSociologyEngineering

Abstract

fetched live from OpenAlex

The life sciences is an industrial sector that covers the development of biological products and the use of biological processes in the production of goods, services and energy. This sector is frequently presented as a major opportunity for policy-makers to upgrade and renew regional economies, leading to social and economic development through support for high-tech innovation. Innovation, Regional Development and the Life Sciences analyses where innovation happens in the life sciences, why it happens in those places, and what this means for regional development policies and strategies. Focusing on the UK and Europe, its arguments are relevant to a variety of countries and regions pursuing high-tech innovation and development policies. The book’s theoretical approach incorporates diverse geographies (e.g. global, national and regional) and political-economic forces (e.g. discourses, governance and finance) in order to understand where innovation happens in the life sciences, where and how value circulates in the life sciences, and who captures the value produced in life sciences innovation. This book will be of interest to researchers, students and policy-makers dealing with regional/local economic development.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.642
Threshold uncertainty score0.917

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.220
Teacher spread0.171 · 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