Bibliographic record
Abstract
Mary’s story is one that is likely being repeated around the globe in the wake of the recent economic downturn. The program Mary refers to in her narrative is called Second Careers, run by the Ontario Government. The program was set up specifically for Ontarians who have been laid-off to train for new careers in high demand areas. Applicants are eligible for up to $28,000 (Canadian) in financial aide to use towards community college-level educational programs. This is Mary’s story of her occupational transition during this major economic recession. I finished high school but at the time never really thought about having a career. It was just all about having fun back then. I had no thoughts about my future. I know I was very lucky to get the job I did. It was because I had family and friends already working at the plant; that’s the only reason I got hired. It sure wasn’t because of my resume. I was aware when I was hired that things were different from when my parents worked. I knew I would not be working there for my whole life, but at the same time it wasn’t something I really thought about in terms of planning what I would do. Even when I started work, the future of the plant was on shaky ground. I worked at a manufacturing plant and earned a decent living. But in August of 2008 things changed quickly and initially for the worse. When the plant starting giving out pink slips I had no seniority so I was one of the first to go. I had worked for three years. I knew I had had it good but now there were just no jobs. Even fast food places weren’t hiring. And I couldn’t compete even if there were jobs. I didn’t have any special skills, so if it came down to me or someone with more education I knew that without personal contacts there was no way I would get hired. And most of my family and friends were soon in the same boat as me; they had all been laid off when the plant closed for good a few months later. It was very fortunate that I had worked for those three years though. I admit I was someone who complained loud and often about taxes, union dues and all the other money that gets taken from each paycheck. I have a very different outlook on that now. I was able to live on the employment insurance money at first but I knew it wouldn’t last very long. I have two children and I wanted them to have the life they deserved. I wanted them to be able to do things without having to think about money. I would have had to give up my car and maybe my house. Plus the prospect of finding another decent job was beyond unlikely. Manufacturing was not going to make a big comeback even when and if the economic situation turned around. Those jobs were gone for good. Through a centre set up by my old union I learned about a program run by the provincial government. The program is for laid off workers to get training in some skill or career at a college. I took this test that tells you what kind of work you would be good at and are interested in. The idea of taking something I was interested in was appealing. I was given enough money to go to school but more importantly I knew that at the end of the program I would have access to a career as opposed to just a job. I am about halfway through the program now and I have such a great outlook. The school helps us to find jobs and there seem to be quite a few. The last few months of the program are a co-op where the students work at a company to learn more practical skills. We are told that most students end up getting hired fulltime by the company they do the co-op for, so that’s encouraging.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.038 | 0.021 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.007 | 0.019 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".