Bibliographic record
Abstract
In this teaching reflection, co-authored by an instructor and a teaching assistant, we consider some of the unanticipated openings for deeper engagement that the “pivot” to online teaching provided as we planned and then delivered an introductory course on Indigenous language documentation, conservation, and revitalization from September to December 2020. We engage with the fast-growing literature on the shift to online teaching and contribute to an emerging scholarship on language revitalization mediated by digital technologies that predates the global pandemic and will endure beyond it. Our commentary covers our preparation over the summer months of 2020 and our adaptation to an entirely online learning management system, including integrating what we had learned from educational resources, academic research, and colleagues. We highlight how we cultivated a learning environment centered around flexibility, compassion, and responsiveness, while acknowledging the challenges of this new arrangement for instructors and students alike. Finally, as we reflect on some of the productive aspects of the online teaching environment—including adaptable technologies, flipped classrooms, and the balance between synchronous and asynchronous class meetings—we ask which of these may be constructively incorporated into face-to-face classrooms when in-person teaching resumes once more.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".