Bibliographic record
Abstract
When schools across this province were ordered to close on March 13, 2020, due to the Covid-19 pandemic, classroom teachers could never have imagined the profound repercussions of this decision and the colossal impact this would have on both teachers and students across Quebec. Teachers scrambled to upgrade their technology skills as school boards quickly mobilized to provide much-needed technical support and hardware for teachers, students, and parents/guardians. More than ever, home and school needed to be connected. In a quick response to the urgent need to provide a continued opportunity for learning, the virtual classroom soon took centre stage. In January 2022, a couple of months before the pandemic sent us into lockdown, I became a McGill Field Supervisor for the Faculty of Education as my career as a secondary school/ adult education ELA teacher would soon come to a close. I had planned to retire that June after teaching for 42 years at the English Montreal School Board. In my three final months at the EMSB, I designed, with a colleague, an online Secondary 5 ELA course, and became very well-acquainted with Microsoft Teams and all it had to offer. The course was both solid and engaging, affording the students the opportunity to work on their own, in small groups in break out rooms, and co-operatively as a whole class. Students were expected to participate and were held accountable for their contributions to the learning experience.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.083 | 0.027 |
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".