Bibliographic record
Abstract
I am pleased to present Issue 6.3. Articles in this issue focus on aspects of teaching. Sara Sohr-Preston and colleagues examine the student rating of professors. In their empirical work, the authors demonstrate that there are multiple factors, some of which are not under the control of the professor, influence student ratings; this suggests that ratings should be used by faculty and administrators cautiously in any administrative decision process. David Giacalone provides results of a study showing the value of case-based scenarios and audience response systems to improve student learning. We are pleased to publish these works that further scholarship related to learning.As we come to the last quarter of the year, I wanted to let you know that, in 2017, we are going to shift our publication strategy somewhat. We are going to reduce to two issues per year, one that publishes in June and the other in December. To ensure that articles are available throughout the year, we will begin publishing on a rolling basis. This means that once we receive a manuscript, and it is accepted for publication, it will be published online right away. Published articles will then be collected and put into an issue twice each year. We hope that this, along with our goal to continue to reduce the time to publication, will allow you to showcase your work right away to the larger academic and professional communities. We thank you for your readership of the Higher Learning Research Communications journal and encourage you to consider our journal for your publication needs.
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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.105 | 0.077 |
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".