Bibliographic record
Abstract
As the public education system in Northern Ontario continues to take a downward spiral, a plethora of secondary school students are being placed in an alternative educational environment. Juxtaposing the two educational settings reveals very similar methods and characteristics of educating our youth as opposed to using a truly alternative approach to education. This video reviews the relationship between public education and alternative education in a remote Northern Ontario setting. It is my belief that the traditional methods of teaching are not appropriate in educating at risk students in alternative schools. Paper and pencil worksheets do not motivate these students to learn and succeed. Alternative education should emphasize experiential learning, a just in time curriculum based on every unique individual and the students true passion for everyday life. Cameron Culbert was born on February 3rd, 1977 in North Bay, Ontario. His teenage years were split between attending public school and his willed curriculum on the ski hill. Culbert spent 10 years (1996-2002 & 2006-2010) competing for Canada as an alpine ski racer. His passion for teaching and coaching began as an athlete and has now transferred into the classroom and the community. As a graduate of Nipissing University (BA, BEd, MEd.) Camerons research interests are alternative education, physical education and technology in the classroom. Currently Cameron is an active educator and coach in Northern Ontario.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.478 | 0.143 |
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".