Bibliographic record
Abstract
The Oxford English Dictionary defines corruption as dishonest or illegal behaviour, especially of people in authority. Education is arguably one of the most important aspects of life as with such a lot of doors are opened for individuals, which are not offered to those without. Generation after generation the power and importance of education is only further highlighted, thus more and more children are pursuing higher education. As such, the number of students enrolled in schooling worldwide has been on an upwards trajectory, with an increase of over 150 million students since 2000 for secondary school and around 82 million since 2000 for primary school (Statista, 2022). Furthermore, the completion rate of primary school was close to 90% in 2019 and 76% for secondary school (Statista, 2022). Education can provide individuals with so many options and aid in their life path by increasing the aspects of stability in life, financial stability, equality between individuals worldwide, self-dependence, confidence, provides safety, and helps one reach their goals (Nair, 2022). In Canada itself, more than 1.4 million students are enrolled in a Canadian University, and 2.2 million students are enrolled in some form of postsecondary education (Universities Canada, 2018).
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".