Bibliographic record
Abstract
In this essay, I discuss the challenges faced by Canadian researchers in trying to undertake research, particularly in the area of education. I begin by focusing on the issue of data availability (with focus on the lack of race data in Canada) and the extreme limitations that these issues place on the potential for research on important Canadian education issues and then discuss what I regard as hypervigilant data access protocols for Canadian data sets. I then turn to practical issues that arise when comparing education data across cities and countries and the process of “harmonizing” the data. I address the compromises that must be made when attempting to make data comparable across different sites. I conclude by discussing how the larger context in which education occurs must be considered when understanding observed comparative differences between educational outcomes.
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 | 0.093 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.029 | 0.038 |
| Scholarly communication | 0.029 | 0.010 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".