Means of survival: youth unemployment and entrepreneurial training in Newfoundland
Bibliographic record
Abstract
Federal and provincial governments first began tracking youth unemployment rates in the 1970s. Since then, the youth unemployment rate has remained in the double- digits both nationally and in Newfoundland. The Federal Government has responded to the problem of youth unemployment in various ways since the late 1960s, most recently in partnership with business interests to promote entrepreneurship as an alternative to traditional paid employment. Because youth generally lack the experience and financing required to open a business, they seem unlikely targets for these messages. This thesis set out to discover what else the promotion of entrepreneurship as a solution to unemployment might be trying to achieve. -- This thesis approaches the promotion of entrepreneurship as part of a government and business sponsored social movement that is in place to promote New Right values. It uses primary documents and newspaper articles to lay out the development of an entrepreneurial movement in Newfoundland. It also explores how and why individuals and organisations become involved in entrepreneurial training, their attitudes towards unemployment and entrepreneurship, and their experiences with these, through the use of semi-structured interviews with participants, instructors and administrators of two youth entrepreneurial training programmes that ran consecutively in two Newfoundland cities. -- The promotion of entrepreneurship is different from past government responses to youth unemployment in that it is in partnership with business interests, and appears everywhere - in newspapers, job sites, television, magazines, radio, and school classrooms. In addition, sponsorship money is available for organisations that want to run entrepreneurial training programmes, and for unemployed individuals who want to learn the skills. The participants in this thesis believe that individual employment circumstances are an individual responsibility, but they also believe that social policies very much shape available choices. All participants in this research used entrepreneurial training as a means of staying in Newfoundland when employment opportunities proved insufficient. For most participants in this research, entrepreneurial training was a source of income and occupation at a time when there were no other employment options available to them. It was simply a means of survival.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".