Bibliographic record
Abstract
In 10 years, I hope that higher education will be really playful, and a space where faculty have more time to be innovative and creative in their approaches.I do not believe it is wise to maintain status quos for over 500 years.Things change, the students change, and the things we are teaching change" (Abegglen et al., 2022, p. 192).I began my doctoral journey and career as an emerging scholar amid the COVID-19 pandemic, where Virtual Learning Environments (VLE) became collaborative spaces and communities of practices that supported the sustained effort needed to abruptly transition to synchronous teaching and learning.The book review request was timely, and as I lingered amid the carefully curated list of books, I paused on Abegglen et al.'s (2022) Voices from the Digital Classroom.Diving in, I sought to highlight areas of tension in VLEs while drawing parallels between Abegglen et al.'s text and my doctoral research exploring the narratives of teachers' VLE experiences in an early elementary context.Abegglen et al. set the parameters of the book in their opening, grounding readers in the Teaching and Learning Network Online (TALON) framework.This section presents as a type of abstract for the chapters to come; however, it does not trouble the reader
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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".