Bibliographic record
Abstract
In 2004, leading testing expert Robert L. Brennan 1 explained: “I failed to recognize that a testing revolution was underway in this country that was based on the nearly unchallenged belief (with almost no supporting evidence) that high-stakes testing can and will lead to improved education.” 2 Despite such cautions from mainstream assessment and measurement scholars, the current frequency and use of standardized testing is unprecedented in US history. In Canada, despite significant variation across its provinces and territories, norm-referenced standardized testing (ST) has scarcely been as widely used as it is today. To be clear, testing is not the only important development underway in education. Despite a push against social foundations in education 3 in some teacher preparation programs, teacher training is generally more comprehensive than it used to be. New teachers are better versed in supporting diverse students, they have access to a greater variety of instruction and assessment techniques and they have a deeper applied understanding of education research and technology than many of their predecessors. However, while testing is not the only driver of change, it is the most significant. Standardized testing is best understood as a technology, the nature and effects of which can be read a number of ways. 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.090 | 0.045 |
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".