Threading Humanity Back into Education and Educational Research
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.
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 | 0.039 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.007 | 0.135 |
| Scholarly communication | 0.027 | 0.038 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".