Bibliographic record
Abstract
Some teachers view assessment as a necessary evil. Some view assessment as their only real tool of discipline and power. Still other teachers view assessment as an integral part of C&I, and the pivotal practice around which teaching methods and communication turns. Most teachers appreciate local, teacher-controlled assessment and loathe the high stakes assessment that produces anxiety, fear, and competitive tactics. For many administrators, parents and politicians, assessment has its justifications in accountability to standards. Indeed, it is difficult to navigate through the various forms of assessment and perspectives on assessment that teachers face on a daily basis. Everyday assessment entails hundreds of observations that teachers make of their students. This involves informal discussions, feedback and deliberate, staged activities and performances. Assessment involves volumes of documentary evidence, from daily assignments, quizzes, and tests to observations, projects, and digital artifacts. In its most stereotypical form, assessment in technology studies simply meant putting a mark on a completed project, much like a merchant places a price on a product. By current standards, this was inauthentic assessment. Since the late 1980s and early 1990s, authentic assessment has transformed the way we think about and carry out assessments in the schools. Technologies of assessment had similar effects.
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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 | 0.046 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.070 | 0.040 |
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".