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Record W1971472924 · doi:10.1145/1215942.1215946

How much do technical scientists really cooperate?

2006· article· en· W1971472924 on OpenAlexaff
Angela C. Sodan

Bibliographic record

VenueACM SIGCAS Computers and Society · 2006
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsTeamworkPersonalityIdeal (ethics)Knowledge managementPsychologyComputer scienceEngineering ethicsPublic relationsManagement scienceSocial psychologyPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Teamwork has evolved to play an important role in science and is called for by funding agencies, universities, and industry. However, as important as teamwork nowadays is, teamwork may not be the only approach to produce relevant research results. Furthermore, cooperation does not always match its ideal and ranges from nominal collaboration to close interaction. Given that researchers in technical sciences are often rated as introverts, the question is whether this is true and how this affects their collaboration style and their capability to solve conflicts in collaboration. This paper presents the results of a study that investigates to what extent researchers collaborate, what their motivation for collaboration is, and how they deal with conflicts. Furthermore, the paper assesses the researchers' personality types and checks whether a correlation exists between collaboration-related choices and personality types. The study was carried out with researchers in the area of high-performance computing.

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)0.010
metaresearch head score (Gemma)0.057
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.998
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.057
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0070.005
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.238
Teacher spread0.229 · 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
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

Citations4
Published2006
Admission routes1
Has abstractyes

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