Bibliographic record
Abstract
I sit here and ponder about the wisdom and knowledge I want to pass on in my guest editorial piece and think back to almost 30 years ago. I was 23 and thought I had the world figured out and ready to make my mark in life. I was certainly naïve enough to think I was prepared to educate the world and anyone else who might want to listen. Fresh out of university, I took the first teaching job offered which was located in a small mining town in north western Ontario. It was about that time someone informed me that I would change my career at least three times in my life span. Since then, I have not only changed my career from secondary school teacher of 24 years, to operating my own consulting business for several years, and then taking on an Assistant Professor role in a School of Education program as my current position in life: I have moved schools and institutions several times. Although life has taken me from coast to coast and institution to institution, I have maintained one facet of my life that has not waivered - that is the opportunity to teach and research.
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 | 0.271 | 0.271 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.015 | 0.078 |
| Scholarly communication | 0.030 | 0.032 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.017 | 0.024 |
| Insufficient payload (model declined to judge) | 0.011 | 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".