Threading Humanity Back into Education and Educational Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we discuss the significance of re-humanizing education and educational research within an AI-dominated era. We also suggest that tactile learning, often overlooked in educational research and digital pedagogies, cultivates unique ways of multi-sensory knowing and encourages holistic understanding, complementing intellectual learning and enriching research processes. Using the metaphors and practices of weaving, knitting, and crocheting, we argue that tactile experiences, especially those involving fiber crafts, create a fabric of interconnections, fostering growth and intellectual expansion. Exploring the applicability of tactile learning in the educational landscape, we examine a number of scholarly works that demonstrate the benefits of integrating fiber craft activities in educational settings across various learning levels. We also delve into the role of researchers as makers and weavers, arguing that the tangible act of textile creation, namely tapestry-making and knitting, encourages reflexivity and allows for revisiting assumptions, refining and deepening meaning-making. We further emphasize the potential of tactile learning as a tool for fostering inclusivity in education and accessibility in the dissemination of research findings. Recognizing the need for academic work to be comprehensible beyond the confines of academia, we suggest the use of tactile representations, such as a woven tapestry, as non-traditional, creative ways to share research outcomes with a wider and more diversified audience. In essence, this paper underscores the potential of a combination of tactile learning and reflexivity in inspiring new insights and threading humanity back into education and educational research.
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.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it