Bibliographic record
Abstract
I cannot feign some distanced objectivity in writing this profile of Peter McLaren. Our relationship dates back to 1989, when Philip Stedman, one of my professors at the University of Cincinnati, invited me to accompany him on a visit to Peter at nearby Miami University of Ohio. I had read some of Peter’s work, particularly some of his early collaborations with Henry Giroux, who had helped bring Peter to Miami from Canada. No amount of reading, however, could have prepared me for meeting him face-to-face. At the time, Peter and his wife Jenny, a beautiful woman possessed of equally great intelligence and compassion, lived an hour’s drive from Cincinnati in the small town of Oxford situated in the middle of southwest Ohio farm country. After we parked the car in front of their modest house, Jenny welcomed us warmly at the door before we ever had the chance to knock. She invited us in and we exchanged introductions for what seemed like a long time. Being so new to academia, I was anxious to meet Peter who was, even then, an important figure in critical educational studies. When he did appear, he too welcomed us warmly, receiving us like we had known each other for years and like our arrival was as much of an event for him as it was for us. As I’ve grown to know Peter over the past fifteen years, I’ve learned to trust and appreciate his immediacy as part of the more general passion with which he lives his life. It’s same passion unmistakably reflected in his writings, and the same passion that generates such tremendous shared loyalty and bonds of solidarity between himself, his students, and others of us who work with him.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.030 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".