Disrupting Boundaries in Education and Research
Bibliographic record
Abstract
In Disrupting Boundaries in Education and Research, six educational researchers explore together the potentialities of transdisciplinary research that de-centres human behaviour and gives materiality its due in the making of educational worlds. The book presents accounts of what happens when researchers think and act with new materiality and post-human theories to disrupt boundaries such as self and other, human and non-human, representation and objectivity. Each of the core chapters works with different new materiality concepts to disrupt these boundaries and to consider the emotive, sensory, nuanced, material and technological aspects of learning in diverse settings, such as in mathematics and learning to swim, discovering the bio-products of 'eco-sustainable' building, making videos and contending with digital government and its alienating effects. When humans are no longer at the centre of the unfolding world it is both disorienting and exhilarating. This book is an invitation to continue along these paths.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".