Bibliographic record
Abstract
This paper investigates some of the practical implications of a phenomenology of education, exploring how this philosophy can have a daily application to work that takes place in the classroom. Beginning with an overview of key principles in the philosophy of phenomenology, including ideas from Edmund Husserl’s and Martin Heidegger’s writings in the 19th-century, the paper examines more recent applications of this philosophy as an approach to pedagogy. The discussion includes the work of Malte Brinkmann and Norm Friesen, and then also that of the philosopher Hubert Dreyfus to consider the ways in which phenomenology would shape a teaching practice. The challenge here involves attempting to form authentic and genuine relationships, despite, or through, the various education technologies and platforms that are available these days. These technologies are often promoted as leading to student “immersion” in the subject matter, while the reality is, at the same time, that the teacher becomes distanced from the students, through the mediating effect of all this media. One example that is proposed as a way to address this issue, involves discussing the example of a class that I teach, where the students need to collaborate to build a playable video game in RPG Maker MV. They become immersed in learning the technology required to create an interactive game, while at the same time remaining in contact with their fellow collaborators as they develop the narrative and game play along the inclusive and enriched story telling principles that are presented in class.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".