Working Conditions for K-12 Distance & Online Learning Teachers in Canada
Bibliographic record
Abstract
“Teacher unions in Canada have had concerns about developments in online learning, but have generally been supportive if they have felt conditions were appropriate,” according to the Director of Research and Technology at the British Columbia Teachers’ Federation (BCTF). This sentiment has been echoed by the researchers involved in the annual State of the Nation: K- 12 E-Learning in Canada. These researchers have also underscored the fact that teacher unions have also been active in conducting research to investigate how teaching in the distance education and online learning environment is different than teaching in the classroom, and what impact that has on the nature of work and quality of work life for its members. The present study is an example of this exploration.\nThis report describes a study conducted to explore written provisions for the working conditions of K-12 distributed learning teachers in Canada (i.e., distance education and online learning are generally referred to as distributed learning throughout the report). At present, there is one provincial jurisdiction that includes language in their collective agreement with teachers related to distributed learning. There are also two provinces where there is language in one or more local contracts focused on distributed learning. Finally, there was one province where the provincial teacher union had a significant policy related to distributed learning.\nWithin these documents, there were consistent themes around 1) defining distributed learning; 2) clauses focused on teacher working conditions in the distributed learning environment; 3) responsibilities for the schools and/or school boards that choose to operate distributed learning programs; and 4) mechanisms to allow for consultations between those operating the distributed learning program and the union. In all of these themes, there are actually few regulations that go beyond what would be expected for traditional brick-and-mortar education. The main areas where distributed learning teachers were treated differently than face-to-face teachers were for legal reasons, as well as the provision for consultations between distributed learning operators and their respective unions. These unique aspects are reflective of stakeholders’ efforts to examine what constitutes the equivalent experiences for teaching in the distributed learning environment relative to traditional classroom teaching.
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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 | 0.000 | 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 | 0.001 | 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) | 0.000 | 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".