Bibliographic record
Abstract
The current public education system in Ontario is seen as successful, having raised standardized test scores and improved teacher training over the past 10 years. These measures of success, however, don’t reflect the changes that will need to be adopted to support learning in the future. Learners are missing out on critical experiences, information and interventions that will better enable them to succeed in the future, because the system is highly resistant to change. \n \nUsing a poststructural foresight methodology known as Causal Layered Analysis combined with the temporal change model of Three Horizons, this research shows that there are deep myth and metaphor level changes required in the system in order to ensure the future success of learners. \n \nOpportunities for innovation emerge in three areas: including student self-reporting of well-being into quality and learning assessment, developing co-operative, community-owned learning spaces for educational and social development, and infusing dialogic design methods and design-led practices into the facilitation of learning. Finally, a theory of change is proposed over a long term, recognizing the resistance to change in the system and introducing the quality of “bounded temporality”, the idea that we may not be able to make the best decisions for the future because we are limited by our blindness to time-based values and orthodoxies that shape what we believe to be obvious.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.087 | 0.028 |
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".