Bibliographic record
Abstract
This paper explores results of a study carried out in a rural context during COVID-19. During the pandemic, emergency remote learning was first adopted in Manitoba, Canada. At the beginning of the following school year, however, schools returned to in-person classes and some students, who had a medical condition that prevented them from going back to schools, needed an alternative. Collaboration from seven distinct school divisions in Western Manitoba made it possible to develop an innovative remote learning program that would serve this population. This action research collected data from parents, students, teachers, curriculum consultants, and principals within this program. Teachers and consultants worked together in the pedagogical and logistical planning so that the inclusion of these families in the remote school would take place in a natural way. Parents of students, who took on a role as co-educators of students from pre-school through eighth grade, were immersed in a remote context and had to adapt to new teaching and assessment systems. Principals maintained contact with the families participating in the program, and helped other professionals with extra administrative functions. Part of the analysis of these data, presented in this paper, led to reflections on the context of innovation in which the program was developed, the inclusion of students in teaching and learning during the pandemic, digital inclusion, and remote communication. Based on the theories of (digital) collaboration, technologies and education, COVID-19 and education, and remote learning, the discussions presented in this paperaddress possibilities, potentials, and challenges of a new meaning of education due to the pandemic.
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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 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".