Bibliographic record
Abstract
Examine creativity through the lens of entrepreneurship. Like creativity, entrepreneurship can have many definitions, but in one reading, Jeffrey Nytch (2016) described it as a mindset. Nytch explains this mindset as steps in a process. First, you will recognize the opportunity in front of you, then focus on the issue, remain flexible and adaptable, and identify resources. This to me, appeared to be similar to creative problem solving. When creativity collides with entrepreneurship, learning to balance in opposition to struggles, as well as always finding new and exciting ways to redevelop and restructure a business to stay ahead of the curve is pivotal to having a business. This can also be said about creativity. Creativity is omnipresent. “Each of us does have an Aladdin’s lamp, and if we rub it hard enough, it can light our way to better living, just as that same lamp lit up the march of civilization”(Osborn, 1952b, p. 8). Creativity can be harnessed and used as a missile or cannon during a war. The potential alone to create opportunity is infinite. Where their potential there needs to exist an action and then we have changed. With the unlimited change that needs to happen, every single individual has a part they must be willing to play to make an impact. Through entrepreneurship, it can take a domestic violence victim or survivor from poverty to plenty, from social services to success, and from minimum wage to Chief Executive Officer.
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 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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.012 | 0.036 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
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".