Bibliographic record
Abstract
This article is an auto-phenomenological curricular exploration represented through the writing of a "postcard from prison". The postcard from prison construct was developed through a series of conversations with the late Donald MacDougall. It is meant to act as an opening and movement towards a reflexive hybrid inquiry combining the autobiographical method of currere and phenomenology, for the purposes of understanding the curriculum in relationship to pre-service teacher education. Framing this inquiry with the idea of a postcard being sent from prison is meant to, arguably, assist the author in making a Husserlian phenomenological satz (leap) from the questions and recollections that have up until this point constituted her perceptions and framing of life events. In the spirit of phenomenological forecasting (Magrini, 2014) and the phenomenological reduction, this auto-phenomenological engagement is an attempt to reawaken the self to ontological questions of Being. The outcome of this undertaking is to consider how to live in the world deeply, with appreciation for each moment and experience without trying to categorize phenomenon, real or imagined, in dualistic terms. Implications of this engagement are considered alongside the themes of education and teaching.
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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.028 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".