Bibliographic record
Abstract
In this article I share my experience of the emergency transition to remote teaching. I discuss the logistics and lessons learnt from transitioning my third-year economics course from in-person instruction to online instruction after the Covid-19 pandemic was declared. I ascribe my constructivist approach to teaching as a key factor which assisted in mitigating stress and allowed for greater malleability in the transition. In the process of the switch to remote teaching, I implemented a three-pronged approach which consisted of a flipped classroom model which facilitates an experiential learning environment with a greater recognition for and an application of kindness in pedagogy. Overall, the emergency transition, though it required a greater expenditure of time, hastened the restructuring of my teaching practice. The verbal feedback from students and the official course evaluation suggest that this approach has the capacity to provide a conducive environment for learning and enhance student experience. This three-pronged approach is suited for both online and in-person instruction. The intention is to continue to apply this approach to both online and in-person teaching. In so doing, it will facilitate the further validation of the efficacy of this approach in providing a conducive environment which engages and motivates student learning.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".