Bibliographic record
Abstract
The Foundations for Academic Success project aspires to improve student engagement with academic material by exploring mobile learning to better resonate with the current student population. Objectives are to develop strategies for enhancing student academic integrity (AI) knowledge and understanding by employing open access mobile technology with an innovative pedagogical approach. Research supports the development, administration, and assessment of the Foundations for Academic Success academic integrity mobile learning (IntegrityMatters) tool that explores best strategies, from a student user perspective, for accessing, delivering, assessing, and learning this information with mobile technology. Academic integrity content in this mobile application extends beyond its utility to University of Waterloo, Ontario Canada students as the values it promotes apply provincially, nationally and internationally. Open-access mobile application has the potential to be adopted and used across many post-secondary colleges and universities. Foundations for Academic Success was made possible by an eCampus Ontario Research and Innovation grant.
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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".