You've got this! The fundamental values of academic integrity
Bibliographic record
Abstract
After so many changes in education over the past year, the need to stay grounded in fundamental values is more important than ever. The surge in cognitive offloading tools (i.e. apps and websites that will offer completed academic work), have educators feel they are running a losing race to keep a diverse student body focused on learning content and demonstrating knowledge with integrity. Integrating discussions on the fundamental values of honesty, trust, fairness, respect, responsibility, and courage in classroom supports has allowed the Academic Success Centre and Library Services at Red River College to build academic integrity into their suite of supports. Session presenters will share examples of collaborative sessions that have empowered students to analyze options and make decisions that lead to academic success. Session participants will be asked to reflect on opportunities to integrate the fundamental values into their work. This session will encourage you to use the resources you have to promote academic integrity with confidence.
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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.025 |
| Scholarly communication | 0.025 | 0.015 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.023 | 0.015 |
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".