Bibliographic record
Abstract
On Being Your Own BossBack in the early days of my nursing career, the possibility of becoming an entrepreneur was not an idea openly considered or discussed among my contemporaries.Undergraduate courses attending to professional issues and roles and responsibilities were silent on entrepreneurial possibilities.In all likelihood, even if we were tempted by the notion, serious consideration of entrepreneurship would likely have been dismissed as a passing phase of youthful and naïve enthusiasm.As a new graduate with little confidence and experience, the proposition of my having capabilities that might be marketable seemed a remote, if not far-fetched, idea.In those early years, working for an organization seemed the most secure and practical option, a circumstance in which one benefited from being surrounded by those more seasoned and experienced.Furthermore, being employed by a healthcare organization afforded a much needed steady and predictable income, benefits and an opportunity to progress in terms of salary and career.While entrepreneurship is not limited to veteran practitioners, cumulative years of life experience, personal and professional, undeniably contribute to one's self-assurance and, to varying degrees, wisdom not otherwise attainable.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.012 | 0.027 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".