Bibliographic record
Abstract
When I first visited the Principles of Learning (Pol) course wiki, I sensed the breadth and depth of the existing posts. The proceeding page has been intentionally left blank. It represents the feeling of dilemma when creating my first, new post. This paper will report on my contributions to the Principles of Learning (PoL) course wiki as a form of reflection on my first semester in the Masters of Education (MEd) program at the University of Ontario Institute of Technology (UOIT). The opening discussion poses the dilemma of first encountering the wiki. The next section describes a series of smaller posts to the wiki while reflecting on the work process of building wiki content. This is followed by a summary of contributions to the wiki featuring people in education: Bill Bigelow (2017), Malcolm Gladwell (2008), John Goodlad (2004), and a discussion of revisionist history. A final section will consolidate what I learned during this process for use in future course work. Anafterword notes additional contributions that I made before 2016 came to a close.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".