A Return to Learning: Re-kindling Education's Love of Learning for Schools of the Future
Bibliographic record
Abstract
Education continues to be a well-researched topic and the importance to reimagine schools of the future could not be a more pressing concern than it is today. As critical tensions steeply arise, such as climate change, systemic inequities, or digital ethics, we must wonder whether the present education system is adequately preparing children for the critical uncertainties of today and of tomorrow. And, as the Ontario education system is facing unprecedented circumstances with the unearthing of thousands of Indigenous children beneath Canadian residential schools, and the school disruptions caused by the COVID-19 global pandemic, the fragility and declining relevance of the institution is being exposed. \n \nInstead of a prescriptive approach on specific strategies and policies that could take place, this study focuses on creatively re-imagining schools of the future through systems analysis frameworks to identify levers of change, paired with a foresight approach utilizing the Three Horizons framework. To inform, validate, and challenge these frameworks, an ongoing literature review took place over the duration of the study, and a small sample size of education system actors was interviewed and invited to participate in a foresight workshop. Together, these efforts aim to explore a response to the question: How might we shift our paradigm of education to chart a pathway forward to re-imagined schools of the future? \n \nA lever of change worth exploring, which is the focal point of this study, is education orthodoxy across system actors. I explore how a shift in fundamental education ideology could have a drastic impact on the education system, possibly producing a preferred future. While I propose that an ideological shift can have far reaching ramifications, I acknowledge that it must be matched with other systemic interventions, but that changing the way we think about education is certainly a critical starting point.
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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.080 |
| Scholarly communication | 0.021 | 0.029 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".