Bibliographic record
Abstract
Careers for Kids! How to Help Your Kids Choose a Career, by Robert Shewchuk. 2013. Winnipeg, Manitoba, Canada: Start Smart Publishing. 157 Pages. Paper. $14.95 CDNThe subtitle of this book is How to Help Your Kids Chose a and it is clearly aimed at parents wanting to help their teens select potential careers suited to their skills, gifts, and interests. This book's chatty, conversational style puts the reader at ease immediately, assuring us you don't have to be a Counselor with years of training and experience to help guide your child through the labyrinth of career exploration. I am a big fan of how this book is organized. Visually, it is a very easy read with plenty of white space on each page, and Mr. Shewchuk has kept this book to a manageable 157 pages. Further, its information has been precisely categorized. There are seven chapters arranged to address each progressive step of career planning, none of them lengthy, and all of them packed solid with current career exploration practices combined with ideas, suggestions, and personal stories.Careers for Kids could have been a hard sell, considering the training, education, and hands-on experience career counsellors are expected to accumulate to do theirjob well. However, Mr. Shewchuk is able to provide enough solid background in basic career exploration that parents can feel they come to this project with a understanding of what is required of both themselves and their teen. For example, he uses the visual of Old Linear vs. New Zig Zag as a model to identify the pattern of career paths in the 21st century compared to those of thirty years ago, and with which most parents would be more familiar. He then introduces the concept of how Labor Market trends work, illustrating as a case in point, how technology has motivated the decline in watch-wearing now that most people have cell phones. His invented phrases are fun and engaging. He uses Career Mashers to describe mixing several interests together to mash a new career, such as a writer passionate about the environment. Another example is Career Moochers which describes discovering support roles in trending sectors such as technology even if one is not tech-savvy.The process of elimination, personality metrics, the making of lists, and research plans are all typical of most career exploration processes. Mr. Shewchuk offers them in such a concise and defined blueprint - including steps on keeping your youth motivated, and suggestions for exploring options within the broader categories such as education and health - that I can see how this would be an enjoyable, informative, and engaging project for both parents and teens. There are some recurring suggestions in the book that repetitive work, manual labour and retail jobs are perhaps not the good jobs to which to aspire.However, in my own career as an Employment Counselor, I have met retail associates and hospitality servers whose passion is delivering superb customer service, and they have honed their craft to such an art that they are actively sought out in their sectors by employers and recruiters. …
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.072 | 0.067 |
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".