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Record W2943373442 · doi:10.1080/23299460.2019.1608615

A responsibility to commercialize? Tracing academic researchers’ evolving engagement with the commercialization of biomedical research

2019· article· en· W2943373442 on OpenAlexafffundabout
Kelly Holloway, Matthew Herder

Bibliographic record

VenueJournal of Responsible Innovation · 2019
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsDalhousie UniversityUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsCommercializationAmbivalenceEngineering ethicsGraduate studentsPublic relationsSociologyPolitical scienceMedical educationPsychologyPedagogyMedicineMarketingBusinessEngineeringSocial psychology

Abstract

fetched live from OpenAlex

Governments and academic institutions have embraced the importance of commercializing research through the late twentieth century. In this study, we seek to understand scientists’ contemporary understanding of the role of academic science in this commercially oriented environment. We present findings based on 30 semi-structured interviews with biomedical researchers from different career stages at a medium-sized university in Canada about patenting, presenting at conferences, creating a company, applying for funding, and interacting with industry. We attend to differences between ‘established’ researchers (faculty) and ‘emerging’ researchers (graduate students and post-doctoral fellows). In general, all participants indicated that commercialization is a normal and mundane aspect of university research. They communicate a considerable amount of ambivalence about commercializing their biomedical research, but stress that the pressure to do so is beyond their control. There was a consensus among most participants that commercialization is the only way to bring innovations in biomedical research to patients; further, it was a responsibility.

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 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)0.307
metaresearch head score (Gemma)0.179
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesMetaresearch, Bibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.3070.179
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0760.271
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.752
GPT teacher head0.640
Teacher spread0.112 · 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; both teacher heads agree on what is shown here.

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

Citations27
Published2019
Admission routes3
Has abstractyes

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