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Record W1593109848

Buying and selling research and development services, 1997 to 2002

2006· preprint· en· W1593109848 on OpenAlexaboutno aff
Julio M. de la Rosa, Antoine Rose, Pierre Mohnen

Bibliographic record

VenueResearch Publications (Maastricht University) · 2006
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessGeneral partnershipPosition (finance)MarketingBusiness administrationFinance
DOInot available

Abstract

fetched live from OpenAlex

Research and development is a crucial activity in the innovation process. Not every firm is in a position to overcome constraints to R&D, such as costs. Those that perform R&D must choose between forming a partnership with other firms, governmental organisations, universities or doing it themselves internally. Others may sell R&D services or buy them. This study provides a statistical portrait of the strategies Canadian companies used in conducting research and development between 1997 and 2002. It is based on data from the Survey of Research and Development in Canadian Industry. During this time period, the majority of R&D spending, around 62%, was of internal origin, that is, it was conducted by the performer. The remaining 38% portion was comprised of two groups: one group representing 24% performed R&D on behalf of another organization, that is, they contracted in. The remaining 14% was conducted by another R&D performer, that is, they contracted out. An estimated 42% of research and development was conducted with no external partnerships. Foreign-controlled firms were much more heavily involved in selling R&D services than their Canadian counterparts. About 22% of all foreign-controlled firms conducted R&D for outside organizations, more than twice the proportion of only 9% of domestic performers. However, Canadian-controlled firms on average spent more on research and development. As a result, the 9% of Canadian-controlled performers allocated 23% of their total R&D spending to selling R&D services, virtually the same proportion as the 25% allocated by foreign-controlled firms.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.939
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.004
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0000.001
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 head0.242
GPT teacher head0.336
Teacher spread0.094 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations6
Published2006
Admission routes1
Has abstractyes

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