Students as Creators, Drivers, Innovators, and Collaborators
Bibliographic record
Abstract
Students amaze us!Teaching can be a demanding, yet a rewarding profession.Our personal experiences with the 2018 University of Calgary Conference on Postsecondary Learning and Teaching and the students that we interacted with were truly amazing.Their insight, their stories, and their presentations provided an opportunity to critically examine and build on our collective knowledge of teaching and learning.This issue of Papers on Postsecondary Learning and Teaching (PPLT) has surpassed its original volume ( 2016) threefold.There are contributions from across Canada and the United States, indicating a growing interest in the University of Calgary Conference on Postsecondary Learning and Teaching and an opportunity to publish in PPLT.The conference theme 'Students as Creators, Drivers, Innovators and Collaborators' has produced a significant body of work expanding on the conference presentations of May 1 and 2, 2018.PPLT's success is due to the many committed reviewers and the dedicated editorial team consisting of Dr.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".