Bibliographic record
Abstract
Philosophical Issues in Curriculum is a class offered twice (Spring and Summer semesters) every two years. The course is designed to normally attend student-teachers whom are finishing their practicums. After much emphasis in “Professional training” for student-teachers, it is believed that they lack a stronger humanistic/philosophical background. Having that in mind, I designed the course in a way that I could invite the student-teachers to think about the difficult aspects of education: how hard it is to work in groups, how difficult it is to manage and organize people and discussions. The readings were all basically on critical pedagogy, and there was a heavy reading load. I chose to require four full-length books and a myriad of suggested readings and films, in order to challenge these student-teachers to do some slow, thoughtful and distraction-free work. One of the most interesting moments of the course was the course syllabus itself. Having experienced some difficulties managing seminar courses in the past, I wrote a lengthy (10 pages!) syllabus to be read in class during week one. I wrote the syllabus in a conversational tone, with some footnotes that added some humour and lightness to the tremendous academic tasks I was inviting them to engage in. Besides the many activities and readings, I asked my students to develop a project without having in mind a final grade. The main pre-requisite of the project was that they designed it to show the community that lives outside of academia. I tried to foster the sense that since we were in a public institution for higher learning, we should develop work and give it back to the people. Out of four groups (of eight students each), two projects are already real and engaged in the world: one is a curriculum on social justice designed by teachers, fighting for the teachers’ right to design their own curriculum, and has caught the Minister of Education’s eye. The other project became a real forum in which educators from diverse fields and backgrounds gather once a month to discuss solutions for education’s problems. This project was worth only ten per cent of their mark, and their final op-ed (with its 750 word max limit) was worth fifty. Paradoxically, they all put way more effort into their projects. Proof of this is that no-one’s op-ed has been published, thus far. I established this grading system in a paradoxical manner as a way for them to reflect upon the true value and/or meaning of grades.
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 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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".