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Collaboration in Academia: Motives, Forms and Impacts on scientific Productivity

2017· article· en· W6926494428 on OpenAlexaboutno aff

Bibliographic record

VenueUbuntuNet Alliance · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainable Development and Environmental Policy
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityPretextThe InternetWork (physics)Scientific literatureFocus (optics)

Abstract

fetched live from OpenAlex

With the Internet becoming readily available and cheaper, even in the most remote corners of the world, opportunities for exchanges of all kinds are more and more wide spread. This is true for the exchange of ideas, results and research methods. Many scientists are taking advantage of these new opportunities by working collaboratively with peers, industry and governments both locally or in further places. The review of the literature on such activities clearly shows that industry-university collaboration in applied engineering receives the bulk of the attention. As well, these previous studies mainly focus on mechanisms and the financial costs and benefits of collaborative research. They thus leave out the impact of collaboration on academic productivity. Some of these studies also claim that collaborative research is just a pretext for generating additional research funds with minimum coordination. Others further contend that increased collaboration by scientists and educators will ultimately bastardize the traditional mission of universities, i.e. producing graduates and generating new knowledge and ideas. While these previous studies shed useful lights on scientists' collaborative activities, by mainly focusing on applied engineering, they are limited in scope. They also overlook many of the motives behind collaborative activities in academia. These omissions are in addition to the limited attention they pay to the impacts of collaboration on scientists' productivity. Using information from a survey of 1,566 scientists from all scientific disciplines in Québec, Canada, our study tries to overcome some of these limitations by i) looking at collaboration by scientists from all scientific disciplines; ii) by accounting and comparing collaborative activities among 1) researchers; 2) with industry; and 3) with other institutions, namely, governments and organized interest groups and finally iii) by assessing the impacts of these collaborative activities on scientific production. The results show that collaboration is prevalent in all scientific disciplines even though scientists in humanities have fewer collaborative output than others. As well, motives for collaboration are of three types: strategic, organisational and operational. Furthermore, even though academic collaboration intended to produce patented or unpatented products, scientific instruments, softwares and artistic products has few output, collaboration ultimately increases productivity, regardless of the discipline or the partners. Thus, university administrators aided by NRENs, government decision-makers and other donors should work at creating and enabling a collaborative friendly environment for academic researchers.

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)0.026
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.990
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.065
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0100.016
Science and technology studies0.0080.007
Scholarly communication0.0140.006
Open science0.0010.011
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.013
GPT teacher head0.272
Teacher spread0.258 · 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 designObservational
DomainIncentives
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
Published2017
Admission routes1
Has abstractyes

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