Bibliographic record
Abstract
Transformative Education for the Second Renaissance follows educator John PW Hudson through a personal and professional journey that led him to respond to what he sees as underlying fissures in the bedrock of educational practice. At the height of his career, he was seconded by the Richmond (BC Canada) school district to teach a demonstration class in the Nanashan Xian Middle School in Shenzhen, China, at the request of the school, and philanthropists Joe and Margaret Li, initiators of the project and sponsors. His assignment was to demonstrate and explain Western teaching methods to educators and other interested parties including university students and their professors from various parts of China, local and national education officials, teachers at the Nanshan Xian middle school (where he lived and taught for two years), and civic officials as well. Most days a television camera was in the room, and several adults sitting watching.Throughout his career, Hudson was intensely interested in how children learn, how and why they thrive or fail, educational philosophy, and how educational infrastructures and practices impact learners and professionals alike. After teaching Music, English and business education for twenty years at the junior high school level, Hudson turned his sights to the elementary level and taught intermediate classroom for the last thirteen years before going to China. All of these experiences left him with lingering questions which came into sharp focus in China, where traditional practices are entrenched.Primarily, Transformative Education for the Second Renaissance explores history, philosophy, research, politics and real human stories to encapsulate the driving forces of education that need adjustment, particularly assessment. Hudson describes the transition from analog to digital as the Second Renaissance, and how findings in brain research characterize how our understanding of learning has changed in modern practice from transmissive to transformative. Not a traditional academic treatise, Hudson’s book reads more like a coffee shop discussion, but the reasoning and conclusions will resonate with experienced educators. Hudson’s goal is to kick-start discussion about the changes he proposes, and frame a narrative to move education into our rapidly changing educational landscape. This is not a book on methods; it is a foundational work that Hudson hopes will lead to lively discussion and critical debate.
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 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 | 0.000 | 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) | 0.002 | 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".