Laboratory Environmental Conditions Influence Patent Inventors’ Creative Self-efficacy
Bibliographic record
Abstract
A comfortable experimental environment usually enables stress relief among inventors, allowing them to focus on inventing. However, to facilitate smooth and continuous experimental procedures, the public spaces and computing environments of conventional laboratories are usually replete with heavy instruments and interconnected wires; consequently, inventors have limited space to conduct complex experiments. These public spaces and computing environments negatively affect the creative self-efficacy (CSE) of inventors. Based on CSE theory and modified information layout complexity theory, in this study, 100 inventors who had obtained patents were recruited. The results indicated that a wireless cloud public space and computing environment positively moderated and enhanced the relationship between low layout complexity and inventor CSE; conventional public spaces and computing environments featuring cables negatively moderated and weakened the relationship between high layout complexity and inventor CSE. More than 40% of participants highly supported using one electronic tablet to manipulate multiple instruments. The results also revealed that approximately 64% of participants did not think they were essential in promoting critical mass in the laboratory. This finding was significantly different from the degree centrality of creativity perspective. Critical indicators of inventor CSE were found to be inventors’ decision-making capabilities regarding innovative research directions and their communication skills with supervisors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".