Bibliographic record
Abstract
As undergraduate students, the fear and uncertainty of our future and career comes full swing. Anxiety seeped its way into my mind during my first year of university. I knew that given the criminal justice systems impact on my family, a career in criminology was not going to be an easy task, but I chose to go against the grain and try it anyway. Criminology was interesting to me; I sought to know more about the process, I needed to know more about the system. At KPU, I had the opportunity to thrive and absorb invaluable lessons. Through the Co-operative Education Program, I had the opportunity to work with the Canada Border Services, Citizenship and Immigration, and WorkSafeBC. My experiences included enforcing legislation, reviewing immigration and refugee files, and building a professional network. I learned about the politics and systematic issues that surrounded these organizations, and the value of a job that I couldn’t wait to wake up to. My last co-op term was with WorkSafeBC, in the Field Investigations Department. I stepped into my office on my first day of work, not knowing I’d never want to leave. The autonomy and creative process I had over my workload was insurmountable. I decided within my first month that this was where I belonged, and my sole purpose became to do anything and everything I could to stay. Today, I stand as a published author, BA Criminology candidate, and a permanent, full-time, WorkSafeBC employee. I learned humility, I cultivated strength, I bleed resilience, and radiate tenacity. This presentation will discuss the avenues of support and opportunity that students have here at KPU and the many ways by which students can maximize their education. I will discuss my experience at KPU and as an employee of WorkSafeBC, in the Field Investigations Department. More specifically, students will walk away learning some practical information on the processes of conducting Internet investigations using open source media.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.025 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.004 | 0.016 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".