Bibliographic record
Abstract
In twenty-first-century schooling, standardized testing (ST) has as much to do with politics as education. In the United States, the Common Core State Standards Initiative (CCSSI) may produce the largest and most complex web of ST in all of history, while simultaneously, parents in record numbers are pulling their children from the tests and public outcry has reached fever pitch across the country. In Canada, a much quieter push and pull is under way. Like other phenomena related to ST, the differences in resistance as well as political bolstering seem quantitatively (rather than qualitatively) different in Canada. The conversation about testing in Canada is a much smaller (and calmer) version of that in the United States. In Ontario, which is heavily invested in ST, teachers and a small but growing number of parents are pushing back with students opting out in small numbers. This appears, roughly, to correspond with less testing and with relatively less teaching to the test than in US contexts. While Ontario teachers primarily expressed concern with questions of the changing classroom and the changing job, many US teachers read the testing landscape with an eye toward politics as well, and many identify a hidden curriculum of testing as far as the profession. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.103 | 0.275 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.020 | 0.039 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.050 | 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".