Freedom and Financial Independence
Bibliographic record
Abstract
Although self-employment is a growing career option in Canada, many career practitioners find it personally difficult to make the leap from being an employee to starting a business. E-mail surveys completed by 13 self-employed career practitioners and counselors revealed six key factors that contributed to their business success: vision and passion, knowledge of the field, strong professional relationships, effective marketing, and solid financial management. The participants also identified several personal characteristics as contributing to their business success: perseverance, endurance, openness to change, integrity, balance, and willingness to take risks. Most respondents identified autonomy as the most enjoyable part of self-employment, followed closely by working with great people and having more lifestyle options. There were mixed findings regarding the financial rewards of self-employment--although some were earning more than when employed, many weren't. The paper concludes with ten steps for building a business, extracted from the survey results. (Author) Reproductions supplied by EDRS are the best that can be made from the ori inal document. Building a Business: 10 Steps to Fun,
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".