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Record W4232858445 · doi:10.1109/wsc.2015.7408168

Evaluating the science-technology interaction in nanotechnology: A simulation-based study

2015· article· en· W4232858445 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venue2015 Winter Simulation Conference (WSC) · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicUniversity-Industry-Government Innovation Models
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceNanotechnologyMaterials science

Abstract

fetched live from OpenAlex

Nanotechnology as an emerging, science-driven and rapidly evolving field with the multidisciplinary nature is an example of cases where science and technology are proximate and their interaction is essential. The scientific and technological networks can be formed separately in a social context and the linkages from the scientific to the technological network can be established through authors-inventors who act as gatekeepers and bridge the knowledge between the two communities. This work concerns individual researchers who are doing both, patenting and publishing, in the field of nanotechnology in Quebec Canada. An agent-based model was developed using real data regarding both nano-related articles and their authors, and nano-related patents and their inventors were collected from SCOPUS and USPTO databases respectively. While the repetitiveness in collaborative relationships has shown an enhancement in author-inventors performance, it negatively affects the knowledge flow efficiency. Author-inventors are fundamentals for increasing the network productivity and assure its interconnectivity.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.158
GPT teacher head0.371
Teacher spread0.213 · 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