Exploring Equity Issues in Education: A Focus on the Rise Private Tutoring in Canada
Bibliographic record
Abstract
Canada has recently seen an explosion in the tutoring industry, in large part due to four key factors: increased immigration from countries where tutoring is popular, the expansion of tutoring franchises from America, parents’ lack of confidence in Canada’s public education, and an excess number of teaching professionals who struggle to find work in a crowded education field. Though the tutoring industry has helped to stimulate the economy, improve student outcomes, and support the transition of immigrants, international students, and refugees, many have concerns that it will widen an already problematic education gap. Thus, to understand the nuances of this issue so as to propose direction for future research and policy, the current study conducted a comprehensive literature review. The results suggest that there are inconsistencies with the pedagogical approaches used by tutoring franchises and the qualifications of the tutors themselves, and in turn the effectiveness and efficiency of tutoring in general.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.021 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".