Educational counter-cultures : confrontations, images, vision
Bibliographic record
Abstract
This book is a song of resistance. Drawing on rich cross-cultural perspectives from Pakistan, Israel, Canada, the US and the UK, the authors challenge readers to envision new ways of thinking for education: ways which draw on imagination, the arts and the collective experience of subjugated cultures and ways of knowing. Michael Apple opens with a critique of the new hegemonic blocks he identifies in US education policy, deconstructing the political and rhetorical moves that effectively maintain the notion of educating in the 'right' way. Mike Cole and Terry Wrigley each examine and rethink what they see as discourses of despair embedded in the opposing paradigms of postmodernism and the School Effectiveness and School Improvement movements. Elizabeth Atkinson and Richard Bond deconstruct two forms of silencing: the silencing of sexualities within educational practice and the silencing of indigenous voices within Canadian Higher Education. Farid Panjwani and Halleli Pinson explore the contested discourses of power and control in religious education in Pakistan and citizenship education in Israel.Jean McNiff and Revital Heimann also focus on Israel, as the context for a reconceptualisation of peace education based on a real recognition of the fractured nature of human relations. Jerome Satterthwaite invites us to see contemporary educational practices through the eyes of both the mystic and the astrophysicist; Alan Bleakley explores learning to 'know' within medical training through the senses; and John Danvers and Victoria Perselli illustrate how poetry, art images and notions of performance can move us towards emancipatory ways of knowing that resist contemporary technico-rationalist discourses. These authors offer multiple perspectives on resistance and they have a common purpose: to see, think and do education otherwise.This book is for all those students, tutors and researchers interested in opposing dominant educational discourses and instead exploring other ways of knowing. It is of deep significance to students at both undergraduate and postgraduate level engaged in education, policy studies and professional training, and to teachers and researchers in all phases of education and training.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.056 |
| Scholarly communication | 0.027 | 0.016 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 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".