Bibliographic record
Abstract
To bridge the gap between the learning goals of the classroom and the overtaxed, returning-from-the-pandemic learner, adapting teaching practices to respond to present-day experiences became a way to facilitate success. Weaving anecdotal experiences with pedagogical scholarship, this discussion explores the impact of practices that approach the learning experience with grace (Su, 2021) and care (Mehrotra, 2021). These practices include the value of putting Maslow’s Hierarchy of Needs before Bloom’s Taxonomy of Learning (Mutch & Peung, 2021), and adopting a trauma-informed approach to create opportunity for all students’ success. This includes: Incorporating opportunities for students to make decisions and exercise choice over aspects of their assignments and facilitating a sense of ownership over their learning (Wolpert-Gawron, 2018), incorporating structured engagement among peers to create a supportive learning community (Lang, 2020), and incorporating practices of instructional care and holistic recognition to build trusting relationships.
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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 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".